ImageMagick v6 Examples --
Resize or Scaling

Index

ImageMagick Examples Preface and Index

Resizing Images

• Ignore Aspect Ratio ('!')
• Only Shrink Larger ('>')
• Only Enlarge Smaller ('<')
• Fill Given Area ('^')
• Percentage Resize ('%')
• Pixel Area Limit ('@')
• During Image Reading

Other Specialised Resize Operators

• Geometry - Resize just the last image
• Thumbnail - Resize with profile stripping
• Magnify - double image size
• Resample - Changing an images resolution
• Sample - Resize by row/column replication/deleting
• Scale - Minify with pixel averaging
• Adaptive Resize - Small resizes without blurring
• Liquid Rescale - Resize using Seam Carving
• Distort/Affine Resizing - free-form resizing

Specific Problems using Resize

• Resizing to Fill a Given Space -- old method
• Resizing Line Drawings

Resize Artifacts - How Good is IM Resize?

Blocking,  Ringing,  Aliasing,  Blurring

• ImageMagick Resize VS Photoshop Resize

Resize Filters

• Interpolated Filters

  Point,  Box,  Triangle,  Hermite
  Other Interpolation Filters

  Gaussian Blurring Filter

  Gaussian,  Quadratic,  Cubic
  Other Gaussian-like Filters

  Filter Support Setting

  Filter Blur Setting

  Sharpen Resized Images -- Photoshop Resize Technique

  Windowed Sinc/Bessel Filters

  Lanczos,  Hanning,  Hamming,  Blackman,  Blackman,  Kaiser,  Bartlett,  Parzen,  Welsh

  Lobes Filter Support Setting

  Lagrange Filter

  Cubic Filters

  Cubic,  Hermite,  Catrom,  Mitchell
  Cubic BC Settings

  Expert Filter Settings

  Summary of Resize filters

  Filter Comparison, and the Best Filter?

We we look at enlarging and reducing images in various ways. The Image remains intact and whole, but individual points of color merged or expanded to use up a smaller/larger canvas area.

Note that while this is related to the resolution of an image (number of pixels per real world length), that is more a product of how the image is eventually used, and not a true concern of Direct Image Processing.

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Resizing Images

The more obvious and common way to change the size of an image is to resize or scale an image. The content of the image is enlarged or more commonly shrink to fit the desired size. But while the actual image pixels and colors are modified, the content represented by the image is essentially left unchanged.

However resizing images can be a tricky matter. Their are a lot of options that you need to consider, and to give you the maximum scope of control ImageMagick provides you with a multitude of options, resize operations styles, and ways of specifying the new size of the image.

The first and foremost thing you should consider when specifying a image to resize is... Do you really want to modify the image?

Resizing will cause drastic changes to the content of the image, and avoiding or minimizing the change should be of greatest importance. Perhaps just a slight Shave of the edges, or a more general Crop of the image will produce a better and more desirable outcome than a wholesale resize of the image. It generally will look better.

The resize operator has been very carefully designed to try to produce the best possible result for real world images. That is not to say you can't use it for diagrams, or line drawings, though for that type of image you may need to use some of the more advanced options we'll look at later.

The resize operator is given an area into which the image should be fitted. This area is not the final size of the image (unless a '!' flag is given) but the maximum size for the final image. IM tries to preserve the aspect ratio of the image more than the final actual size for the image. That is, a circle in the input image will remain a circle in the output image.

So let me be clear...

For example here I attempt to fit two source images, one larger image and one smaller image into a square box 64x64 pixels in size.

As you can see a 64x64 square image was NOT produced by "-resize". In fact the images were only enlarged or reduced enough so as to best fit into the given size.

Ignore Aspect Ratio ('!' flag)
If you want you can force "-resize" to ignore the aspect ratio and distort the image so it always generates an image exactly the size specified. This is done by adding the character '!' to the size. Unfortunately this character is also sometimes used by various UNIX and DOS command line shells. So you may have to escape the character somehow to preserve it.

Only Shrink Larger Images ('>' flag)
Another commonly used option is to restrict IM so that it will only shrink images to fit into the size given.   Never enlarge.   This is the '>' resize option. Think of it only applying the resize to images 'greater than' the size given (its a little counter intuitive).

This option is often very important for saving disk space for images, or in thumbnail generation, when enlarging images generally may not desirable as it tends to produce 'fuzzy' enlargements.

The Only Shrink Flag ('>' flag) is a special character in Window batch scripts and you will need to escape that character, using '^>', or it will not work. See Windows Batch Scripting for this and other windowing particularities.

Only Enlarge Smaller Images ('<' flag)
The inverse to the previous flag is '<', which will only enlarges images that are smaller than the given size, is rarely used.

The most notable use is with a argument such as '1x1<'. This resize argument will never actually resize any image. In other words it's a no-op, which will allow you to short circuit a resize operation in programs and scripts which always uses "-resize". Other than that you probably do not actually want to use this feature.

One such example of using this 'short circuit' argument is for the "-geometry" setting of "montage". See Montage and Geometry, caution needed for more details.

Fill Area Flag ('^' flag)
As of IM v6.3.8-3 IM now has a new geometry option flag '^' which is used to resize the image based on the smallest fitting dimension. That is, the image is resized to completely fill (and even overflow) the pixel area given.

As it stands this option does not seem very useful, but when combined with either a centered (or uncentered) "-crop" or "-extent" to remove the excess parts of the image, you can fit the image so as to best fill the area specified. Both the resize and the final image size arguments should be the same values.

Though the "-crop" is most logical, it may require an extra "+repage" to remove virtual canvas layering information. The "-extent" does not require this cleanup, but only allows the use of "-gravity" for positioning. See Cutting and Bordering for more information.

Also "-extent" can be used to pad out images that use the normal resize (with a "-extent" color setting). See Thumbnails, Fit to a Given Space Summary, for more on this type of operation.

Remember this requires IM v6.3.8-3 or greater to make use of it. Otherwise use the older Resizing to Fill a Given Space technique below.

The Fill Area Flag ('^' flag) is a special character in Window batch scripts and you will need to escape that character by doubling it. For example '^^', or it will not work. See Windows Batch Scripting for this and other windowing particularities.

Percentage Resize ('%' flag)
Adding a percent sign, '%', to the "-resize" argument causes resize to scale the image by the amount specified.

Be warned however that the final pixel size of the image will be rounded to the nearest integer. That is, you cannot generate a partial pixel image!

If you really want to resize image such that the final size looks like it has a partial pixel size differences, you can use the General Distortion Operator and specifically the Scale-Rotation-Translate (see Distort Resizing below).

The Percentage Resize Flag ('%' flag) is a special character in Window batch scripts and you will need to escape that character by doubling it. For example '%%', or it will not work. See Windows Batch Scripting for this and other windowing particularities.

Resize a Pixel Area Count Limit ('@' flag)
There is one final "-resize" option flag. The "at" symbol '@', will resize an image to contain at most the the given number of pixels. This can be used for example to make a collection of images of all different sizes roughly the same size. For example here we resize both our images to a rough 64x64 size, or 4096 pixels in size.

Note that the final image size is not limited to 64 pixels in height or width, but will have an area that is as close to this size as IM can manage.

All these 'flag' options '!', '<', '>', '^', '%', and '@' are just on/off switches for the "-resize" operator. Just the character's presence (or absence) in the resize argument is what matters, not their position. They can appear at the start or end of the argument, or before or after individual numbers (though not in the middle of a number). That is, '%50' has exactly the same effect as '50%' though the latter is preferred for readability. Also '50%x30' actually means '50%x30%' and NOT 50% width and 30 pixel high as you might think. This is the case for all IM arguments using a 'geometry' style ('WxH' or '+X+Y') of argument. However offsets such as '+X+Y' are never treated as a percentage.

Resize During Image Read
The resize operator can also be applied to images immediately after being read, before it is added to the current image sequence and the next image is read. That way a minimal amount of memory is needed to read in a lot of images. See Image Read Modifiers for more details.

For example...

The only problem with this technique is that no special resize options can be used, during the image read process.

Resize and transparency posed a problem for ImageMagick before v6.2.4, producing a black halo effect around light colored objects on transparency. This was researched and finally fixed from that version onward. For more detail of this old bug see Resize Halo Bug

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Other Resize Operators

Geometry - Resize just the last image

Geometry is a very special option. The operator behaves slightly differently in every IM command, and often in special and magical ways. The reasons for this is mostly due to legacy use and should be avoided if at all possible.

First, in "display" it is used to size and position the window of the image being displayed. This was its original usage and meaning when IM was first started. It was from this that its other 'resize' capabilities came about.

For "montage" "-geometry" is a setting that is saved until all the arguments have been read in. At this point it then defines the final tile (cell) size (or leaves it up to "montage" to work out) while the position arguments are used to specify the space surrounding the tile cells. See Montage Control Settings.

In "composite", "-geometry" is also saved until the end of arguments have been reached. Then it is used to resize and position the overlay image (the first image given) before it is overlaid onto the background image (the second image). For example see Composite Multiple Images.

As you can see it is used as a 'setting' in most IM commands, but in "convert" "-geometry" is both a special image resizing operator and a positioning setting.

What it does is to "-resize" just the last image in the current image sequence. This is the only image processing operator that is designed specifically to effect just the one image (the last one), in the current image sequence.

To complicate this special option further, the positional parts of the "-geometry" option is saved by "convert" command, just as it is in "composite". That is, any position is preserved for later use by the "-composite", to position the 'overlay' image, (the second last image in the current image sequence) over the 'background' image (the first image in the image sequence).

For this reason, you should limit the use of "-geometry" in "convert" commands to just before a "-composite" or "-layers composite" operations.

To summarize, this operator is only really useful after reading or creating a second image, just before you perform some type of Alpha Composition to process with those images.

For practical examples of using "-geometry" to resize/position images see Compositing Multiple Images.

Thumbnail - Resize with profile stripping

The "-thumbnail" operator is a variation of "-resize" designed specifically for shrinking very very large images to small thumbnails.

First it uses "-strip" to remove all profile and other fluff from the image. It then uses "-sample" to shrink the image down to 5 times the final height. Finally it does a normal "-resize" to reduce the image to its final size.

All this is to basically speed up thumbnail generation from very large files.

However for thumbnails of JPEG images, you can limit the size of the image read in from disk using the "-size" setting, so the extra speed improvement is rarely needed for JPEG in thumbnail generation. But it is still useful for other image formats, such as TIFF, or for its profile stripping ability. As such it is still the recommended way to resize images for thumbnail creation.

Before IM v6.5.4-7 the "-thumbnail" would strip ALL profiles from the image, including the ICC color profiles. From this version onward the color profiles will be preserved. If the color profile is not wanted then "-strip" all profiles.

Magnify - double image size

The "-magnify" option just doubles the size of an image using the "-resize" operator. Plain and simple. It is rarely used.

A "Minify()" function is also often available in API's that halve the size of images in the same way as the "Magnify()" function of those API's. However "-minify" is not available from the command line API, at least not at the time of writing.

Resample - Changing an image's resolution

Just as in the previous alternative resize operators, "-resample" is also a simple wrapper around the normal "-resize" operator.

Its purpose however is to adjust the number of pixels in an image so that when displayed at the given Resolution or Density the image will still look the same size in real world terms. That is, the given image is enlarged or shrunk, in terms of the number of pixels, while the image size in real world units will remain the same.

It is meant to be used for images that were read in from, or will be written out to, a program or device of a particular resolution or density. This is especially important for adjusting an image to fit a specific hardware output device, whether it is a display, or printer, or a postscript or PDF image format of a specific resolution. Just remember the real world size of the image does not change, only its resolution and of course the number of pixels used to represent the image.

For example, suppose you had an image that you scanned at a 300dpi (dots per inch). The image was saved with this resolution (density) or when you read it into IM, you specified it as a 300dpi image (using "-density"). Now you decide to display it on a screen that has a resolution of 90dpi, so you do a "-resample 90". IM will now resize the image by 90/300 or to 30% of the images original size and set the images new density to 90dpi. The image is now smaller in terms of the number of pixels used, but if displayed on a 90dpi display will appear at the same physical size as the original image you scanned. That is, it now has a resolution appropriate for a 90dpi display, so it will be displayed to the user at its original real world size.

A "-units" setting (with arguments 'PixelsPerInch' or 'PixelsPerCentimeter') may be required in some situations to get this operator to work correctly. This setting can also be important for output to Postscript and PDF image file formats.

Note that only a small number of image file formats (such as JPEG, PNG, and TIFF) are capable of storing the image resolution or density with the image data.

For formats which do not support an image resolution, or which are multi-resolution (vector based) image formats, the original resolution of the image must be specified via the "-density" attribute (see Density Image Meta-data) before being read in. If no density attribute has been set IM will assume it has a default density of 72dpi. Setting the density AFTER reading such an image will only affect its output resolution, and not affect its final size in terms of pixels.

Sample - Resize by row/column replication/deleting

The "-sample" resize operator is the fastest resize operator, especially in large scale image reduction. In fact it is also even faster than the "-scale" operator (see next).

When enlarging or magnifying an image, they both do pixel replication to generate rectangular 'blocks' of pixel colors. However when shrinking an image "-sample" just simply deletes rows and columns of pixels.

Because whole rows and columns of pixels are simply removed, "-sample" will generate no new or additional colors. This fact can be important for some image processing techniques such as resizing GIF animations.

However directly deleting pixel rows and columns can result in rather horrible results, especially for images containing thin lines (in terms of width in pixels).

For example, here I draw a line but then reduce the image size resulting in only a line of dots. This is a typical effect of image sampling.

Scale - Minify with pixel averaging

The "-scale" resize operator is a simplified, faster form of the resize command.

When enlarging an image, the pixels in the image are replicated to form a large rectangular blocks of color. Which is great for showing a clean unblurred magnification of an image.

For example here is a magnified view of one of the built-in tile patterns...

convert -size 8x8 pattern:CrossHatch30 -scale 1000% scale_crosshatch.gif

Generally a single percentage that is a multiple of 100% is used for the image enlargement so as to ensure all pixels are enlarged by the same amount, otherwise you can have different size pixel rows and columns producing large scale Moiré pattern.

For example here I badly scaled a smooth looking '50% gray checks' pattern, using a size that was not a multiple of the original images size. convert pattern:gray50 scale_gray_norm.gif convert pattern:gray50 -scale 36 scale_gray_mag.gif

When shrinking images, neighbouring pixels are averaged together to produce a new colored pixel. For example scaling an image to 50% of its original size will effectively average together blocks of 4 pixels to create a new pixel (assuming the image size is a multiple of 2 as well).

Caution is advised however as a scale reduced image can also generate Moiré patterns, unless the new image is an exact integer reduction (a technique known as 'binning'), which also requires the original image size to be some exact integer multiple of the final size.

Also a real-world photograph that has been heavily minified using "-scale" tends to look overlay sharp, with aliasing ('staircase') effects along sharp edges.

Finally, Cristy reports that the algorithm is designed to loop over rows of pixels then columns, which is inverted to that of "-resize". This may allow "-scale" to handle a "mpc:" disk cached image better.

While this image resize operator is completely separate to the "-resize" operator to make it faster, the "-resize" operator can generate the same results by using a 'Box' Resize Filter (see below).

Up until IM v6.4.7 the "-scale" still contained the old Resize Halo Bug was discovered and fixed.

The pixel averaging of "-scale" allows it to generate 'pixelated' images you typically see used for hiding faces, or 'naughty' parts of images. You basically reduce the size of the image to average the pixels, then enlarge again back to the image's original size.

You can use a mask to combine the above pixelated image with the original image, so as to 'hide' a much smaller 'naughty' bit present in the original image. See the Protect Someones Anonymity example for a demonstration of using this technique.

Adaptive Resize - Small resizes without blurring

The "-adaptive-resize" operator uses the special Mesh Interpolation method to resize images.

For example here I resize a simple line, using first a normal "-resize", then again using "-adaptive-resize". If you look at a magnification of the two results... You can see the "-adaptive-resize" image on the right is a lot cleaner looking and less blurry than the image produced on the left using the normal "-resize" operator.

Basically the operator avoids the excessive blurring that a "-resize" operator can produce with sharp color changes. This works well for slight image size adjustments and in particularly for magnification, And especially with images with sharp color changes. But when images are enlarged or reduced by more than 50% it will start to produce aliasing, and Moiré effects in the results.

These effects were originally noted by dognose in a topic on the IM Forums.

I've noticed that it can be significantly faster, up to twice as fast on big resizes. I've also noticed that the resulting image can be a lot different. It seems that adaptive-resize makes the new image much sharper than regular resize.

For thumbnail generation, the sharpening is too strong, resulting in some aliasing effect being added to the resulting image. It is thus better suited to small scale resize adjustments such as generating a smaller image for display on web pages.

You can also generate the exact equivalent result using a Distort Resize operation but with the options "-filter point -interpolate mesh".  That is, resizing the image using a simple Mesh Interpolation lookup method, rather than a more complex resize filter.

Liquid Rescale - Seam Carving

Just as Sampling an image resizes by directly removing or duplicating whole columns and rows from an image, the special IM operator "-liquid-rescale" also removes or duplicates columns and rows of pixels from an image to reduce/enlarge an image. The difference is that it tries to do so in a more intelligent manner.

Firstly, instead of removing a simple line of pixels, it removes a 'seam' of pixels. That is, the column (or row) that could zig-zag through the image, at angles up to 45 degrees.

Secondly it tries to remove seams that have the 'least importance' in terms of the image's contents. How it selects this is in terms of the image's energy, or more simply, the amount of color changes a particular 'seam' involves. The 'seam' with the least amount of changes will be removed first, followed by higher 'energy' seams, until the image is the size desired.

For more detailed information of liquid resizing and seam carving see, Wikipedia: Seam Carving, the YouTube Video Demo, and the PDF paper: Seam Carving for Content-Aware Image Resizing.

Here for example is the IM logo as it is resized smaller using the IM "-liquid-rescale" operator.

Notice how "-liquid-rescale" preserved the complex wizard, while squeezing up the less complex stars and title part of the image. It also squeeze the right foot of the wizard slightly, producing a little jaggedness in the edge of the cloak, just as it did to the wizard's thin but simple wand.

On the other hand the Sample Resize image, simply removed equally spaced columns of pixels, which resulted in the whole image becoming equally distorted. The stars are not preserved intact and all the edges have distinct but uniform aliasing effects.

Basically "-liquid-rescale" will produce a generally better looking 'squeezed' image without generating extra 'mixed colors' or blurring of the image. However you can get some slight but localized aliasing effects in one spot rather thna spreading that effect across the whole image.

It will also expand images, by 'doubling' up the seams found within the image.

As you can see tries to first double the amount of space between the various objects (where it can), spreading them out. Though in this case the left most star and the 'm' becomes distorted as the 'seams' going through these 'low energy' regions become grouped together.

Note however it will only double each seam once, and as such the technique starts to break down when images are expanded too much. A better method is often to resize the image larger first, then use liquid rescaling to reduce it to the desired size. Or to use "-liquid-rescale" in multiple smaller steps.

To show the effect of "-liquid-rescale" better here is an animation, as the same image is resized all the way down to nothing, then enlarged again. This animation was created using the shell script animate_lqr.

Again notice how it tries to preserve the most complex parts of the image, as the image gets compressed into a smaller and smaller area. That is, the spaces in the title are preferentially compressed first, then the wizard's arm, then the right side of the wizard, leaving the most complex middle part of the wizard for the very end.

You can think of liquid rescaling as trying to compress an image, like a sponge, with the open areas being compressed first leaving the bulky and more structured parts for last.

Seam carving Problems

Liquid Resize, or Seam Carving, works purely by removing whole pixels from the image. As such, like sampling, it will not generate or merge colors together, and straight lines and patterns within the image may become heavily distorted by the operation. That is, it can result in serious Aliasing effects, unless some method of smoothing is also applied. Generally however the aliasing effect will be grouped and localised, rather then spread thought the image.

As a 'seam' can zig-zag through the image, the seams doubled or removed, can appear to go around complex objects, removing the space between the objects before attempting to compressing the objects themselves. Note for example how the word 'Image' in the above demonstration appears to get shoved under the other letters in the title without too much distortion.

However for images with 'busy' backgrounds, and less 'busy' foreground objects such as photos containing peoples faces, the energy function can assume that the foreground object is less important that the background. This results in some serious detrimental side effects, that may require human intervention to resolve.

Liquid Rescaling, is currently a highly experimental operation added for IM v6.3.8-4. It is also not enabled by default, requiring you to build your own version of ImageMagick with the "liblqr" delegate library before it will work for you. At this time no expert user controls have been provided. Controls such as the content energy function used, a user provided preservation/removal filter (adjusting that energy function), or access to the intermediate images, and functions that the library also provides. It is assumed that such controls will be provided in the near future, as users demand them, and we get more internal control of the library functions. WARNING Do not expect this to remain, as is. It is highly experimental, and is expected to change in the near future.

Distort/Affine Resize - free-form resizing

All the above resize methods all have one limitation which we touched on earlier, they will round the size of the new image to an integer number of pixels, then map the old image's pixels to the new pixel array.

This has two effects. First when resizing to a very small size the X scale may not exactly match the Y scale of the resulting image (a slightly different aspect ratio). This difference is minor, and unless you get very small it is usually not noticeable.

The other effect is that you cannot resize an image to fit an area that contains a partial pixel edge, which can be important in further processing, such image overlays.

It also means you cannot use resize to just shift (translate) an image half a pixel to the right (without actual resize) even though the algorithm could quite easily do this.

With IM v6.3.6 the General Distortion Operator "-distort" will let you do this and more, using its Scale-Rotate-Translate distortion method. You can also do this using an Affine distortion based on movements of control points.

Note however that because the edge of the image can contain partial pixels, the final image will probably be 2 to 3 pixels larger than you probably would expect. The extra surrounding pixels will be mixed according to the current Virtual Pixel setting, which you typically set to be transparent.

For example here I resize the rose image to 90% (.9) of its original size, without rotation (0), shrinking it around the center of the image (the default control point if not specified)...

convert rose: -matte -virtual-pixel transparent \ +distort SRT '.9,0' rose_distort.png

It may not look like an improvement, in fact it has fuzzy edges, but it is an exact resize without adjustments for a final integer image size, just as you requested. Because of this the edges are fuzzy as the pixel colors are being spread over fractions of a pixel size, and not just to whole integers.

Note that I used the 'plus' form of "+distort" to allow this image's processing operator to set the final images size and offset on the Virtual Canvas correctly, for further processing and layering. If this offset is not desired it can be removed using "+repage" operator.

Here I resized it so the top left corner (0,0) was moved .5 pixels to the right (to .5,0) and the rest of the image scaled around that control point...

convert rose: -matte -virtual-pixel transparent \ +distort SRT '0,0 .9 0 .5,0' rose_distort_shift.png

Note that as the top edge did not actually move, it remained relatively sharp, while all the other edges became fuzzy. And that is the point, you have exact control of the resize, not just a quantized fit of the resized image to an integer number of pixels. That is, the distort is an exact re-scaling of the image to fractions of a pixel, and not just a rough fit to the nearest integer for the specified scale factor.

Other General Distortion Operator methods, such as 'Affine' and 'Affine_Projection' distortion methods, provide other ways of controlling the final image size based on the movement of control points. As a result using "+distort" can be a very versatile resize technique.

Technically, image resizing is a form of Image Distortion, both of which are techniques of image resampling. It is just that "-resize" is a more limited (orthogonal or axially aligned, and to the nearest whole integer dimension), and is coded to provide a much faster form of the more general "-distort" operator.

The "-distort" operator uses a two dimensional elliptical (distorted cylindrical) resampling filter method for its image processing. This is slower than the one dimensional, two pass resampling method used by all the other resize methods looked at in this section. But it also allows us to generate higher quality results of more severe image distortions such as perspective views.

As of IM v6.4.2-8 the older "-affine" setting and "-transform" operator, provide a similar free-form resize capability. However in reality it is equivalent to a calling "+distort" with an 'AffineProjection' distortion method. As such the previous notes apply.

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Resize Problems

Resizing to Fill a Given Space

Basically: Resizing a large image to completely fill a specific image size but cropping any parts of the image that do not fit.

As of IM v 6.3.8-3 a new resize flag '^' will let you do this directly as a single resize step. These examples represents an alternative method that can be used for users with older versions of IM. See Resize Fill Flag above.

The solution is rather tricky, as the normal user requirement when resizing images is to fit the whole of an image into a given size. As the aspect ratio of the image is preserved, that leaves extra, unused space in the area you are trying to fill.

Here we try to resize an image to fill a 80x80 box.

convert logo: -resize 80x80\> \ -size 80x80 xc:blue +swap -gravity center -composite \ space_resize.jpg

In the above we added a backdrop canvas to pad out the unused parts of the resize box to show the space we wanted the image to fill, but it wasn't filled, as it preserved the image's aspect ratio.

Now if all your images are either landscape style (they are wider than they are high) then you can of course just resize the image to fit either the height or width of the area, then use "-crop" to cut the image to fit it exactly.

convert logo: -resize x80 \ -gravity center -crop 80x80+0+0 +repage space_crop.jpg

The problem is that, the above will only handle landscape style images. It will fail badly if the image is portrait style (higher than it is wide).

This of course can be solved in a script by first getting the image's dimensions, and then picking the right method to fit the image into the space needed. But a better solution would be to have IM do all the work for all images. The solution within IM is to process the image by resizing each of the images dimension separately. Then picking the larger image of the two results.

To make this easier, resize itself has a built-in test option which will only resize an image if that would make the image larger. This allows use a very nifty solution to our problem.

convert logo: \ -resize x160 -resize '160x<' -resize 50% \ -gravity center -crop 80x80+0+0 +repage space_fill.jpg

In the above, the second resize in the series will only resize if the width produced by the first resize was smaller than the area we are trying to fill.

The specific order of the resizes (height first, then width) was chosen, as most images are photographs which are usually longer horizontally. With the above ordering, such a case will result in the second resize operation being skipped.

If your images are more often portrait images (longer vertically) then change the arguments to resize the image by height first, then width. For example...

convert logo: \ -resize 160x -resize 'x160<' -resize 50% \ -gravity center -crop 80x80+0+0 +repage space_fill_2.jpg

The result of both of these examples should be very similar, and the command will work for both landscape and portrait styles of image, though it works better for one sort.

The biggest problem with this method is that the image is now being resized 2 to 3 times, producing extra blurring and other possible artifacts in the final result. To reduce this, the initial resizes are performed at double the final dimensions, which assumes the original image is at least 3 or more times the size of the final desired result. Not a problem for thumbnail production, but something to keep in mind.

Resizing Line Drawings

Doing a strong resize of an image containing thin lines can represent a big problem...

Resizing images to very small thumbnails, causes thin lines that are only a few pixels wide to fade and disappear into the background. This can get so bad that I have seen thumbnails of a line drawing which looked pretty much blank. That is, every detail of the original drawing 'disappeared'.

If this is problem there are a few techniques that can help...

• Resize then adjust contrast to make lines more visible. Though this will make the lines more aliased and staircased, and does have its limits.
• Blur and threshold the image (a method known as morphological 'dialate' or 'erode') so as to produce make single pixel lines about 300% thicker. Now after resizing by 1/3 the image will be smaller but the lines will remain just as strong and visible as before.
  If you need to resize my more than this, you can do the thickening and resizing in multiple increments. though as the spacing between the lines shrinks you may find you end up with more of a 'blob' than a line drawing, sort of the oppiste effect. Adjusting the thickening to resize ratio's however should produce an acceptable result.

• Separate line edging from areas of solid colors, and resize the different parts in different ways so as to improve the results. Afterward the two parts can be merged back together again.

If you come up with some way of effectively resizing line drawings please let me (and other IM users) know about it.

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Resize Artifacts - How good is IM Resize

Image resizing has to combat a very difficult problem. How do you reduce an array of values, into a smaller, or larger array of values so that it look good to our eyes. A lot of things can go wrong while attempting to do this, but they fall into four basic categories..

Blocking

Essentially, if you enlarge an image simply by replicating pixels, you will create larger rectangular blocks of pixels. In fact magnifying images using either "-scale" or "-sample" does exactly that, producing an enlarged pixelated image.

For example here I scale a small image, generating large blocks of color rather that a nice smooth image. Next to that is a 'resized' version, and finally one with a Gaussian filter to blur it a little more than normal to try to remove some of the blockiness. The primary cause of 'blocking' is either badly anti-aliased source image (as in the above example), or not enough smoothing (color mixing) between pixels to improve the overall look of an image.

It is also typically seen when a very low resolution image is being resized to a much larger scale or for use on a high resolution device, such as shown above. Typically the most common place this is seen is in the use of a low resolution bitmap image in user generated newsletters and magazines that were then printed on very high resolution laser printers. The newsletter looks great on screen, but 'blocky' on the printed page.

This situation is very hard to fix, and generally best avoided, by using a much higher resolution clipart, or a freely scalable vector image (such as SVG, and WMF format images).

Ringing

Ringing is an effect you often see in very low quality JPEG images close to sharp edges. It is typically caused by an edge being over compensated for by the resize or image compression algorithm, or a high quality filter being used with a bad support size.

Here for example I use a special option to select a raw Sinc filter, on an very sharp color change. I also repeated the operation using IM's default resize operator, with its default filter selection for image enlargements. The above shows quite clearly the over compensation produced by the use of a raw resize filter, without any of the optimization IM provides. The second image, produced by the default IM enlargement filter also shows a very slight ringing effect, but it is barely noticeable.

Here is another example of the ringing effect, this time as produced by a single pixel, on a large gray background.

convert -size 1x1 xc: -bordercolor '#444' -border 4x4 \ -set option:filter:filter Sinc -resize 100x100\! \ dot_sinc.gif

This image also clearly shows the secondary effects generated by the use of a one dimensional filter. That is, the ringing effect is strongest in horizontal, and vertical (orthogonal) direction, with 45 degree secondary ringing.

These effects are not normally visible, and only seen here because of the use of the use of a raw 'Sinc' filter with enlargements. Typically this type of filter is not used for image enlargements.

Aliasing and Moiré Effects

Aliasing effects are generally seen as the production of 'staircase' like effects along edges of images. Often this is caused either by raw sampling of the image such as using "-sample", or overly sharpening of the image during resizing. A staircasing effect is most noticeable in strong minification of images, though is rarely seen in IM.

However, aliasing also has other effects, in particular large scale Moiré patterns appearing in images containing some type of pixel level pattern. These low level patterns often produce large scale Moiré patterns, including: patterns of parallel lines, cloth weaves (silk exhibits this effect in real life!), as well as brick and tile patterns in photos of brick buildings, fences, and paving.

For some examples of resized images producing strong Moiré effects see the Wikipedia, Moiré Pattern Page.

The classic way of checking if a resize will produce aliasing problems, is by minifying a Rings Image (see right). This image will often show Moiré effects when any form of resize is applied at any scale. Web browsers in particular show such Moiré effects when displaying such an image due to the use of a ultra fast resizing technique.

Here I show the 'rings' image resized using the strongly aliasing "-sample" operator, the block averaging "-scale" operator and the normal default "-resize".

As you can see all the resize methods did produce some Moiré effects, though IM's default resize operator produces the least amount of this undesirable secondary patterns in the final image.

To show the effects of only a slight resize, I cropped the corner from the Large Rings Image, the result of which is shown first, and then reduced its size by just 5%.

As you can see even a slight resize will show up any aliasing a resize operator may have. In fact if you look closely you may even seen a very light Moiré effect in the unscaled crop of the original starting image, which is produced from the limitations of only using a raster image at a density suitable for display on a comupter screen. That is how sensitive this test image is in showing aliasing effects caused by shrinking images.

Blurring

Most people are familiar with blurring that can be generated by the use of "-resize". In fact this is probably the number one complaint about any resize image, and with good reason. Usually a very small resize will tend to produce a blurred image, and resizing it again will only make it worse.

The problem is that when you resize an image you are changing the image stored as a 'grid' or array of pixels (known as a 'raster') to fit a completely different 'grid' of pixels. The two 'grids' will not match except in very special cases, and as a result, the image data has to be modified to make it fit this new pattern of dots. Basically it is impossible to directly resize an image and expect it to come out nicely, though a reasonable result can be achieved.

The result is a usually a slight blurring of the pixel data. The better the resize algorithm, the less blurring of sharp edges there is.

However some resize filters, especially ones designed specifically for enlarging images, often add a lot more blurring than necessary. This is to combat 'Blocking' artifacts, and was in fact demonstrated above, by using a 'Gaussian' filter.

For image minification however a blurred edge is often used to avoid 'Ringing' artifacts at sharp edges and reduce possible Aliasing effects. This however is a poor man's compromise and one that IM tries hard to avoid.

Even so, a special expert Filter Blur setting can be used to adjust the blurring that a filter provides. However be warned that while a number smaller than 1.0 is supposed to reduce blurring, it can also make it worse, depending on the exact filter and the resize ratios that are being used. No guarantees can be given.

Before IM v6.3.6-3 the Filter Blur setting was called "-support", which was very misleading in exactly what it did. This option has been depreciated and is no longer available.

The better method of fixing bluring effects cause by resize is to re-filter the image using a sharpening operator. See Sharpen Resized Images below for more details.

IM Resize vs other Programs

A practical comparison of IM's default resize operator to a number of other programs in resizing a real-world image has been provided by, Bart van der Wolf at...

Specifically, in summary for IM resize...

Although the amount of sharpening is a matter of taste, the lack of aliasing artifacts produces the cleanest, most natural looking image of them all.

He also goes on to look at a 'rings' test, to directly compare various Photoshop resize methods against ImageMagick...

These articles shows just how important doing resize correctly (and using the right filtering methods) is to image processing. We look this at more closely in the next section.

WARNING: These filter comparisons were made before IM Resize filters were overhauled for IM v6.3.7-1, and as such the results for Windowed Filters such as 'Hanning' and 'Blackman' are incorrect.

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Resize Filters

The "-filter" setting is the key control on how the "-resize" algorithm, as well as "-distort", works to produce a clean result with the minimum of Resize Artifacts, as shown above.

This has been a topic of intense study during the late 1980's, and from which Paul Heckbert, a major researcher in this field, produced and publicly released his "zoom" image resizing program. This program became the father of most image resizing programs used today, though few programs implement it properly, as it is easy to get it wrong.

In many ways, these filters are closely related to Blurring Images and even suffer from the similar problems. However they are designed to improve the final result when resizing or otherwise distorting an image.

The names of the filters are a veritable "who's who" of image processing experts and mathematicians of the past century (or more). They are usually not a description of the filter, but just a label of the person who either first published the filter (or filter family), or did the most research into that filter. This however makes it much harder to know whether a 'Lagrange' filter is better than, say, a 'Catrom' (Catmull and Rom) filter.

Here I will explain the major aspects of Filters for those that want to know. It is not vital that you learn these things, but I decided to document a summary of what I learnt, after completing a major research study, and an overhaul and expansion of the IM filter system, with added expert controls (IM 6.3.7-1).

Special thanks goes to Fred Weinhaus for his help in researching during the re-development of the Resize Filters. He was especially eager for the addition of the 'Lagrange' family of filters.

How filters work

When resizing an image you are basically trying to determine the correct value of each pixel in the new image, based on the pixels in the original source image. However these new pixels do not match exactly to the positions of the old pixels, and so a correct value for these pixels needs to be determined in some way.

What is done is to try to use some type of weighted average of the original source pixel values to determine a good value for the new pixel. The real pixels surrounding the location of the new pixel forms a 'neighbourhood' of contributing values. The larger this neighbourhood is the slower the resize. This is a technique called Convolution.

The amount each real neighbouring pixel (known as a 'sample') contributes to produce the final pixel is determined by a weighting function. This is the 'filter' that you can select using the "-filter" setting. That filter in turn generally has an ideal neighbourhood size, which is known as the filter's 'support', though it is also known as its 'window'. A pre-defined two dimensional 'filter' is also known as a 'convolution kernel'.

FUTURE: some diagrams may be helpful here

The design of these weighting functions, or 'filters' is a very complex business involving some complex mathematics, frequency analysis, and even Fourier transforms. A good starting point if you are interested in this is Wikipedia: Nyquist–Shannon sampling theorem. However, you really don't need to go that far to understand existing filters and their effects on images.

The Filters

Interpolated Filters

The simplest type of resize filter functions are Interpolative methods. These take a specific pixel location in the source image and try to simply determine a logical color value of the image at that location based on the colors of the surrounding pixels.

As there is only ever a fixed and minimal number of pixels involved, this type of filter is a very fast method of resizing or otherwise distorting images. However, this is also the filter's downfall, as it will not merge a larger number of pixels together to form an image that is greatly smaller than the original image. That in turn can result in strong Aliasing and Moiré Effects.

Interpolation is usually only used for 'point' sampling images, when image scaling is either not known or needed. For example, when rotating image or minor distortions, the image's scaling or size does not change, and as such an interpolation can produce a reasonable result, though not a very accurate one. For more information see IM's Interpolation Setting.

It is not however suitable for general image resizing.

Point

Using a "-filter" setting of 'Point' basically means to use an unscaled interpolation filter. For the Resize Operator, it will just select the closest pixel to the new pixels position, and that is all. But for the Distort Operator it will force the use of interpolation for areas where the image shrinks, as its normal use in areas of enlargment (see Image Filters in the Distort Operator for more detail).

This means that when shrinking an image, the color of an actual pixel in the source image will be used. No attempt will be made to merge colors or generate a better color for the resulting image. As a result using "-filter point -resize" will produce the same result as "-sample", though the latter is faster as it code is specifically optimized for resizing images by point sampling.

As you can see, even at this level, you will get extreme blocking and aliasing in the resulting image, and can in specific cases like the above produce a completely unrealistic result. As such, a 'Point' filter, or the faster Sampling Operator, is not recommended for normal image resizing.

Box

The 'Box' filter setting is exactly the same as 'point' with one slight variation. When shrinking images it will average, and merge the pixels together. The smaller the resulting image the more pixels will be averaged together. In other words the filter is 'scaled'. The "-scale" resize operator was again optimised to do exactly this.

Here is a graph of the filter's weighting function, from which you can see why it is called a 'Box' filter.

Basically any pixel that falls inside the 'Box' will be directly used to calculate the color of the new pixel. Now as the filter is only 1/2 a pixel wide, and that means for an image that is not actually being resized, only one pixel the closest pixel, will be used. In other words when no scaling is involved (or only magnification) the nearest pixel to the new location will form the color of the new pixel.

However if an image is being made smaller, more of the source image will be compressed into the bounds of that 'box'. The result is that more pixels will be averaged together to produce the color for the pixel in the smaller image.

For example here is an enlarged view of a checkerboard pixel pattern as it is being slowly compressed using a 'Box' filter.

As you can see, more and more pixels become merged together as the image is resized smaller using a 'Box' filter, but that the merger occurs in specific, equally spaced, rows and columns. This causes all sorts of artifacts and Moiré or Aliasing effects when both shrinking images and enlarging.

This is also why it is recommended that 'Box' filtering or image "-scale" is only used for 'binning' images, that is, reduce images by integer multiples to ensure that every pixel in the result is an average of the same number of neighbouring pixels (the 'bin'). The resulting image will thus remain clean looking, just as in the final image above.

Of course both 'Point' and 'Box' filters will produce the same 'pixel replication' method for enlarging images, as both will result in a simple 'nearest-neighbour' selection.

Triangle

The 'Triangle' or 'Bilinear' interpolation filter just takes the interpolation of the nearest neighbourhood one step further. Instead of just directly averaging the nearby pixels together, as 'Box' does, it weights them according to how close the new pixels position is to the the original pixels within the neighbourhood (or 'support' region). The closer the new pixel is to a source image pixel, the more color that pixel contributes.

This produces a more global averaging of colors when images are being reduced in size.

As you can see as the corner pixels were near perfect matches to the corners of the original image they are more visible, but toward the middle where none of the nearby pixels exactly match up with the new pixel being generated, you get a more average color of the whole neighbourhood.

The result for the checkerboard pixel pattern is a tendancy to fade in and out of a average gray color.

However as the 'support neighbourhood is larger, more pixels will be involved when you enlarge the image. Thus producing averaging of colors when generating the pixels being added to the image.

For large scale enlargements the result acts as if a gradient of colors was added between each and every pixel. For example, here I generate a very small image with a single white pixel (the display is an enlarged view). I then enlarge that image enormously.

If you were to graph the colors in the above image (using the "im_profile" script), you will see a replica of the triangular filter graph.

As you can see the central pixel was merged with the neighbouring pixels to produce a linear gradient of colors between those points.

All the interpolation filters, produce similar gradient patterns between neighbouring pixels and is also the reason why they are so well suited to image enlargements.

Other Interpolation Filters

To the right I have graphed the various interpolation filters, except for 'Point' which is a very special case of 'Box'.

Other interpolation filters include 'Hermite' which is very similar to triangle in results, but producing a smoother round off in large scale enlargements. Click on the graph on the right to see a graph of these three filter functions.

The 'Lagrange' filter has been called a 'universal' interpolation filter. By varying the 'support' size (See the support expert setting below), it can generate all the previously looked at interpolation filters (except 'Hermite'). The default settings (a Lagrange order 3 filter as shown as the purple line) provides a variation of the 'bicubic' type of interpolation. (see below).

As an interpolation filter the default 'Lagrange' filter (order = 3, support = 2.0) works very well, though with some minor ringing effects. However the sharp gradient change is often notable on very large scale image enlargements.

More on the Lagrange Filter later.

The 'Catrom' (Catmull-Rom) filter is another filter that produces a 'bicubic' type of interpolation function over a larger area. This filter will also produce a reasonably sharp edge, but without a the pronounced gradient change on large scale image enlargements that a 'Lagrange' filter can produce. This in turn will reduce the amount of noticeable blocking effects, but does so at the cost of increased ringing effects in the resulting image.

We will also look at this filter more closely later in Cubic Filters.

Interpolation and IM's Interpolate Setting

The Interpolate setting of IM which is used to produce an unscaled 'point' lookup of images in operators like the FX DIY Operator ("-fx") and Color Lookup Replacement Operator ("-clut"), and some older Circular Distortion functions are based on these simple interpolation resize filters. However they are currently implemented using separate code and also have different setting names.

These Interpolation Settings include: 'NearestNeighbor', implementing the 'Point' (or unscaled 'Box') filter, and 'BiLinear' to get an unscaled 'Triangle' filter.

ASIDE: At this time the smoothed triangle filter 'Hermite' has not been directly implemented as an Interpolation Setting, which is a shame as it is quite a good interpolation filter. The 'Catrom' filter is also not available.

However there is some confusion as to just what resize filter should be used to implement a 'Bicubic' (16 pixel interpolation) Interpolation Setting. Many programs implement the 'Catrom' filter to produce a smoother gradient but with more ringing, while others, including IM, implement the 'Lagrange' (Lagrange order 3) filter.

Before IM version 6.3.5-3, the Interpolation Setting 'Bicubic' was based on a very blurry 'B-Spline' filter (the IM 'Cubic' filter, see below). That interpolation setting has now been renamed to 'Spline', with the 'Bicubic' setting now based on the default Lagrange-3 (support=2.0) filter as discussed above.

Gaussian Blurring Filters

In the complex mathematics of Fourier Transforms into frequency domains, resize filters are meant to remove any high frequency noise that may be present. This noise is caused by the sampling of a real world image into pixels, and when you resize an image, that noise appears as aliasing and Moiré effects.

Because of this the Gaussian Bell Curve became a natural early candidate as a resizing or resample filter, as it is the ideal model of real world effects.

Gaussian

The Gaussian filter is a very special filter that generates that same 'bell curve' shape in the frequency domain. This makes it very useful as an image filter as it guarantees a good removal of this high frequency noise in a highly controllable way.

However if you examine the filter graph, you will see that at a distance of one pixel from the sampling point, you have a non-zero value. In fact it is quite a high value indeed. This results in a huge amount of blurring of the individual pixels, even when no resize is actually performed. In fact you would get the same result as a Blur operator.

For example here I have resized the standard IM logo using a Gaussian filter and again using the normal IM filter ('Lanczos' in this case, which we will look at later) If you look closely you will see that the left 'Gaussian' filtered image is more blurry than the normal resize. Especially with regard to the detail of the smaller stars around the wand and on the wizard's hat.

This blurring of the image is the trade off you get for removing all the aliasing effects in image reduction, as well as all blocking effects on image enlargement. It will also, never produce any ringing effects (when applied perfectly). But all that is at the cost of extreme blurring for the resulting image.

In fact, during large scale enlargements, this filter will generate round dots, rather than square looking dots. For example, here I greatly enlarge a 3x3 pixel image with a single dot in the center. convert -size 3x3 xc:yellow -fill red -draw 'point 1,1' \ -filter Gaussian -resize 100x100 -normalize dot_gaussian.jpg

As you can see a single pixel enlarges into perfectly circular dot. Only Gaussian and Gaussian-like filters will do this.

Other Gaussian-like Filters

If you study the comparative graphs to the right you will see that 'Quadratic' filter as well as the slightly more complex 'Cubic' filter follow the weighting curve of the 'Gaussian' filter quite well. And being polynomial functions they are also a lot faster to calculate, which was why they were originally invented.

Actually both the 'Quadratic' and the 'Cubic' filter will produce a slightly more blurry result. The later being the most blurry of all the filters provided directly by IM. Examining the graphs you will see that unlike the Interpolation Filters they have a non-zero value at a distance of 1.0 from the sampling point. This causes the nearby pixels to merge their colors, and is the cause of the blurring you see. The 'Cubic' filter having the highest value at the 1.0 distance producing the largest amount of blurring.

The 'Mitchell' filter is also shown in the comparison graph. This filter also has a some blurring at the 1.0 distance from the sampling point, also making this filter slightly blurry in comparison to the other filters we have seen. However it also has some negative weighting in its curve, which while producing ringing effects (see Window Sinc Filters later), which offsets that bluriness in stronger side reductions.

Basically the 'Mitchell' was picked by subjective testing as being a compromise between all four general Resize Artifacts (See Cubic Filters below). As such while it is slightly blurry it isn't overly so. The same goes for the other Resize Artifacts. That is it is a good comprimise filter.

It is also the normal default filter in most situations (except shrinking without transparency), though it may not always be the best filter for all resizing tasks.

Filter Support Expert Option

The gaussian filter also has one important property, the effects of which we have already seen above. This filter is known as a IIR (Infinite Impulse Response) filter, which simply means it never reaches zero. That is, no matter how far away from the sampling point you get, you will still have some non-zero contribution to the result from very distant pixels.

In mathematical terms this is actually a good thing, as it means the result is much more mathematically perfect. In practical application it is very bad, as a infinite filter requires you to use a weighted average of every pixel in the original image, to generate each and every new pixel in the destination image. That means that large images will take a very very long time to resize correctly.

However for the 'Gaussian' filter anything beyond a range of about 1.5 pixels from the sampling point will generally produce very little effect in terms of the final result, and as such can be generally be ignored. In fact if you look closely at the previous graph of the default 'Gaussian' filter line you will see that the curve suddenly 'stops' at a distance of '1.5' from the sampling point. This range is known as the filter's 'support' and is the program's practical limit for the filter.

If you really want, you can change the 'support' of a filter using the special expert setting "-set option:filter:support {value}". For example here I resize a image with a single pixel using a smaller support value of 1.25 (see the resulting graph right).

This modified filter was then used to enlarge a small image with a single white pixel in the middle.

By using the smaller 'support' setting, the 'step' was moved to the 1.25 position. That in turn, results in a larger 'stop' in the filter's profile, and results in an 'aliasing' effect that you can see close to the center of the enlarged image (the wiggle near the 'peak' of the graph) as well as a sudden 'drop' at the edges of the filter's 'support' limits.

You can think of 'support' as being a sliding 'window' across the pixels being averaged together to produce the enlarged image result. As the support size is 1.25, the filter's total support area is 2.5 pixels wide (unscaled during image enlargements), as such you can have either 2 or 3 pixels involved in the horizontal resizing phase.

As each pixel enters or leaves this support 'range' as it slides across the image being generated, the sudden 'stop' in the support causes a slight jiggle to appear in filter-weighted average that is returned. That is, at these points, a pixel is being added or removed from the total number of pixels being averaged together according to the filter weighting curve.

This in turn produces four such 'jiggles' or 'zig-zags' in the resized image, An initial two on the outside edges when the single white pixel enters/leaves the support range, and a second pair of jiggles as a second black pixel (making a three pixel weighted average) enters/leaves the support range.

If there wasn't such a sudden 'stop' in the filter, that is, the filter goes to zero at the support limit setting, then you would not see the 'jiggles' and you would not have the visible 'aliasing' effect.

Also using a support size set to an integer or half-integer (such as the default 'support' setting for a 'Gaussian' filter of '1.5') would always ensure that a new pixel becomes part of the filtered average, another is leaving, so that the same number of pixels is always part of that average. That would remove the two 'center' jiggles, though not the initial jiggles on the outside, marking the support limits.

Previous to IM v6.3.6-3 the 'support' for the Gaussian filter was set to this value of '1.25' producing Ringing effects in enlargements (such as shown above). For this reason the 'support' for gaussian was changed to produce a much smaller step (and far less ringing) by using larger default 'support' of '1.5', with very little speed reduction in the algorithm.

Note however that if you use a very large support setting then of course more pixels will need to be averaged together making the resize operation slower, without any real improvement in results. Only the windowed-sinc and Lagrange filters can generally produce a better result by using a support factor that is larger than 2.0.

Remember these are 'expert' options, and as such you are more likely to make things worse rather than better by using these options. That is why they are not a simple command line option, but provided via the special "-set" option.

Filter Blur Expert Option

A special setting "-set option:filter:blur {value}" can be used to adjust amount of blurring that a filter provides. A value of '1.0' producing the default action, while smaller and larger values adjust overall 'blurriness'.

For example, here I use a simple UNIX shell script to resize an image with three different blur settings...

As you can see, this special setting will let you control the overall blurriness of the result for 'Gaussian' and the other Gaussian-like filters (see above). If fact using a value of '0.7' will make most Gaussian-like filters produce a very good result, with a more acceptable amount of blur.

However reducing the blurring of the filter will enhance the aliasing effects, thus more likely to generate large scale Moiré effects from low pixel level patterns.

Using this setting with other filters containing negative weightings (basically any of the filters we will look at below) can produce more blurring instead of less. Caution and expertise is required to use this special option with non-gaussian like filters.

Before IM v6.3.6-3 the 'option:filter:blur' setting was mistakenly called "-support", which was very misleading in exactly what it did. This option has been depreciated, and may produce warnings when used in all future versions of IM.

Sharpen Resized Images -- Photoshop Resize Technique

Rather than reducing the blur setting of a filter (see above), the better technique is simply Sharpen the image after the resize has been complete.

Typically this is done using the special and weirdly named, Unsharp Operation, which contains even more controls to control the quality of the results.

For example, lets 'unsharp' the results of the very blurry 'Cubic' filter, and compare it to using the Blur Expert Filter Option...

As you can see sharpening the image after the resize produces far better results than trying to use the Blur Expert Filter Option. You get a very good sharp image without any aliasing or ringing effects.

This method of sharpening (actually 'unsharping') works for ANY filter, provides more controls to fine-tune the sharpening effects, and is recomended method.

In actual fact this is what 'photoshop' does to improve the quality of its resized images, though I do not know what settings it uses for the Unsharp Operation. Does any one know?

Windowed Sinc/Bessel Filters

Sinc/Bessel Perfect Filters

Mathematics has determined that the ideal filter for resizing images is either the Sinc() function or the Jinc() function, depending on the purpose the filter is being used for (see Nyquist-Shannon sampling theorem).

Here is a graph of these two infinite weighting functions.

The Sinc() being mathematically perfect has some special features that I would like to point out. First at every integer distance from the weighting function for the filter becomes zero. This is very important as it means that the filter does not blur the image more than necessary (unlike Gaussian Filters).

The other major difference between this and previous filters is that some of the weightings have negative effects. That is, they will subtract some of the nearby color pixels from the final color in each pixel in the image.

This may seem a little strange but it results in a very strong sharpening of the edges of objects. Unfortunately any negative weights need to be offset by positive weights which produces the wave like function you see. This in turn causes ringing artifacts in images which contain lots of sharp boundaries, such as line drawings, if the filter is applied improperly.

The Jinc() function (more commonly known as a 'Bessel' filter) is the equivalent filter for use in a two dimensional cylindrical, or radial filtering operation, though it is not a perfect fit in this regard. Though very similar and closely related to Sinc() (see graph) it is designed to filter a rectangular array of values using a radial or cylindrical distance, rather than orthogonal (axis aligned) distance.

It is impossible to create a perfect fit between a radial distance and a rectangular array of values, but it does produce a reasonably good result, and makes it work better for images which are not only being resized but also rotated and or sheared by some non-orthogonally aligned angle. As such it is the best filter to use for the Distort elliptical resampling method.

This form of windowing filter will automatically be selected when a operator needing a cylindrical or radial filter is selected. At this time only the General Distortiong Operator requests such a filter, though some future Convolution Operators operators may also make a similar selection.

However for normal orthogonal resizing, a windowed Sinc() will be used, and you can just think of these filters as being based on that function.

Windowed Sinc/Bessel

Unfortunately both of the these functions are also IIR (Infinite Impulse Response) weighting functions. This means that to use them, you would need to generate a weighted average of every pixel in the image (and beyond) in order to create the best representation for each and every new pixel in the destination image. This is prohibitively expensive, making the direct use of these perfect filters impractical.

Unfortunately, unlike the Gaussian Filter, these functions do not just taper down to near zero a short distance from the sample point. In fact, even at 10 pixels away from the current sampling point, you can get an appreciable effect on the final result. However resizing an image using a filter that has a support distance of 10 would require an averaging of at least 20x20 or 400 pixels per final pixel generated. That is very slow.

As a consequence, IM does not allow (direct and simple) access to either the Sinc or the Bessel weighting functions in its raw infinite form. That is not to say it cannot be done (see Expert Filter Settings below), it is just not directly available using the simple predefined "-filter" setting.

What is provided instead are 'windowed' forms of these filters and techniques, which have been developed by image processing experts, that can be used to 'limit' the infinite Sinc and Bessel functions to a more practical size. These Windowing Filters include: 'Blackman', 'Bohman', 'Hanning', 'Hammming' 'Lanczos'. 'Kaiser', 'Welsh', 'Bartlett', and 'Parzen'.

How Windowed Filters Work

For example, the graph to the right shows three functions (click to get an enlarged view). The red function is the mathematically ideal Sinc() function, which stretches off to infinity. The green function is a "Hanning" windowing function (based on a simple Cosine() curve . This is multiplied with the Sinc() to modulate the more distant components of the filter, reaching zero (or near zero) at the edge of the support window (typically 4.0 in ImageMagick).

Basically by selecting 'Hanning' for the "-filter" selection you are in reality selecting a 'windowing function' for either the Sinc() or the Jinc() (Bessel) function, depending on what type of filter is needed.

As such, 'Windowed Filters' are really two functions. Either the Sinc or the Bessel function (according to the needs of the operator that will be using the filter), and the 'windowing function' you have specifically selected. (See Expert Filter Controls below).

Before v6.3.6-3, IM made the grave mistake of actually using the windowing function directly as the filter's weighting function. This in turn caused all these filters to produce rather badly aliased images, when used for resizing. As a consequence the filters were often mis-understood or rarely used. This has now been fixed.

The Various Windowing Filters

To the right is a graph of all the various windowing functions that IM has available at the time of writing. Yes, there are a lot of them, as windowing functions have been the subject of intense study by numerous signal processing experts.

Each of the windowing functions will be expanded to cover the 'support' range that is being used for the windowed-sinc filter.

And to the right is the resulting windowed-sinc filters that would be used by selecting those window functions. All of the windowed filter functions will generally use a support of 4.0 for Sinc (4 lobes) and 3.238 for Bessel (3 lobes). The 'Lanczos' filter is an exception (default support is 3.0), but is graphed (see right) using a 4.0 support for comparison purposes.

As you can see all the windowed filtered functions produce a muted form of the original Sinc() function that is also shown. And other than the amount of ringing a specific filter generates there is often very little to distinguish one windowed filter from another.

Probably one of the best windowed filters is 'Lanczos' which uses the first 'lobe' of the Sinc/Bessel function to window the Sinc/Bessel function. That is, the filter's weighting function is used to set the filter's own windowing function.

Other good windowing filters, which are all based on similar ideas of using refined Sine/Cosine functions for windowing include 'Blackman', 'Bohman', 'Hanning', and 'Hamming', all of which make reasonable windowed filters and are quite common within the many image processing packages.

The other windowing filters: 'Welsh', 'Parzen', 'Kaiser', and 'Bartlett', are based on using other windowing methods, many of which are also used as resampling filters in their own right. For example the 'Bartlett' windowing function is actually the 'Triangle' (or 'BiLinear') interpolation filter.

The pre-defined 'Sinc' and 'Bessel' "-filter" settings are windowed using the default 'Blackman' windowing function. As such, selecting 'Sinc' as a filter for a "-resize" operation is equivalent to specifying a 'Blackman' filter. However you will always get the actual underlying weighting function (Sinc(), or Jinc() respectively) instead of that function being selected according the type of image operation being performed. As such, selecting 'Sinc' or 'Bessel' as a "-filter" setting is NOT recommended, unless you are doing something very special (like graphing window functions).

You can look at more detailed definitions and graphs of the various windowing functions, and their results in the Fourier Frequency spectrum on Wikipedia, Window function.

Windowing Effects (Lobes)

As I mentioned, the underlying Sinc/Bessel Weighting function is actually infinite in size. Though by default IM limits them using the specified windowing method to a much smaller, more practical size.

However there may be some situations where you really want to try and get a much better, more exact resizing of the image using a much larger, and slower window (support) size. That can even be done quite simply using the Filter Support expert control, just as we did for Gaussian-like filters.

The windowing function itself will (in most cases) reduce the Sinc/Bessel to zero over the support setting range. But as the windowing function is scaled to fit the 'support' window, the resulting filter function will also change.

For example, to the right I have graphed the 'Lanczos' windowed filters, against the Sinc() function as a reference, using various 'support' settings from 2 to 8. Note that the actual size of the filter is limited by the actual support size used. The smaller the 'support' the faster the filter, but the less exactly the function follows the mathematically ideal Sinc() function.

Look closely at each of the graphed curves. The 'green' (support=2) curve only has the main central peak, plus one negative 'lobe'. After this the function is just zero, and not used. The next 'purple' (support=3, and the default) curve, has a much larger first negative 'lobe', then a smaller positive 'lobe'. This continues on with more lobes being added, as the support size increases by integer increments. The additional lobes are smaller and smaller in height, producing less and less influence on the final result, but with the initial 'lobes' becoming higher (more influence).

For best effect you would use an integer as a support setting, to generate a filter with that many up/down 'lobes' in it. Though the final few 'lobes' will generally have very little impact on the final result. By default the 'Lanczos' filter will have 3 'lobes'. Most the other Windowed Filters default to 4 'lobes'.

However while the Sinc() function has a 'lobe' for each integer 'support' setting, the Jinc() or Bessel weighting function does not. This presents a problem for users wanting to adjust the support window for a filter being used with the Distort operator. In fact, Jinc() has 'zero crossings' at highly irrational numbered positions. These zero crossings are very difficult to work out without being a mathematical expert.

To make it easier to set a filter in terms of the number of 'lobes' another special setting was created, "-set option:filter:lobes {integer}'.

If the filter is being used by a 2 dimensional image resampling operator such as the General Distortion Operator, which requires the use of the Bessel weighting function, it will look up a table of the first 20 zero crossings for the filter, and set the 'support' setting to that value. This means you don't have to try to find the appropriate support setting for the Bessel function, just specify the number of lobes you want to use.

In most cases this acts exactly like a normal 'support' setting, except that it only accepts an integer argument.

Just as an example of using a larger 'support' size, or 'lobes', here I resized the IM logo image in various ways, and zoomed in on a small section so you can look at the details of the resulting image.

The first image is a direct resize of the image using a 'Hanning' windowed filter. This is a base-line image to compare against. The next image was also resized in the same way, but in 15 small steps. This enhances any image artifacts that may have been generated by the filter. Finally I repeat the process again but using a much larger 8 'lobe' version of the filter. The default for 'Lanczos' is 3 lobes.

FUTURE: substitute an example of an incrementally rotated image, rather
that a resize image for the above.  This should produce a better example of
why a resize filter and enlarged support can be so important.

Look very closely at the pixels for the binary numbers on the wizard's hat. The middle image with the smaller default number of 'lobes' in the filter blurred the numbers together, while they are much more distinct in the first and final image.

Unfortunately as the image used is very cartoon like, the ultra sharp boundary around the wizard's hat produced some ringing effects that, while present in all the images, was much more visible in the final image.

Lagrange Filter

Just as the 'Gaussian' filter is a mathematically slow function (not that it affects the overall speed very much thanks to IM's caching of results), the Sinc/Bessel Windowed Filters are even slower and more complex to compute due to the need to compute both a weighting function and a windowing function.

Because of this the 'Lagrange' filter generates a piecewise cubic polynomial function to approximate a windowed filter. (See Wikipedia: Lagrange Polynomial). Just as Windowed Filters are adjustable according to the Support Setting, the 'Lagrange' filter also will adjust itself according to that setting.

The default support setting of 2.0 generates a 'Lagrange' filter (order 3) that is commonly used as a 'Bicubic' interpolation method. This filter is quite good for both enlargement and shrinking of images. with minimal blocking and ringing effects and no blurring effects.

With larger support settings, the 'Lagrange' filter generates Windowed Sinc Filters without needing a complex trigonometric function calculation, or even additional windowing functions. The larger the support setting the closer the filter emulates a Sinc() function, but also the slower the calculation. (see graph of larger support Lagrange filters left).

Using smaller support settings and the 'Lagrange' filter emulates most of the various Interpolated Filters. That is, a support size of '0.5' will generate the 'Box' filter, and '1.0' a 'Triangle' filter.

The support setting is limited to adjustments by half-integers, and using any other support factor is not very productive.

The other half-integer support, Lagrange Filters (generating even orders), produce a very disjoint set of filter weightings, and much like the 'Box' filter, they can produce some strong blocking resize artifacts. On the other hand for small scale resize this can ensure that images keep sharp for very small resize adjustments.

These 'even' ordered 'Lagrange' filters actually highlight the main disadvantage of using this filter, which is that the weighting function is not a 'smooth' gradient. In large scale enlargements this means you can get visible changes in the generated gradient. This is rarely a problem however, except in those extreme cases.

Basically it represents a filter that universally emulates the best filter for the current given 'support' setting, regardless of how big or small that setting is. It is 'the' self-windowing resize filter.

The Support Expert Control is really defining the 'order' of the Lagrange filter that should be used. That is, the default 2.0 support Lagrange filter, generates a Lagrange filter of order 3 (order = support × 2 - 1, thus support=2.0 => Lagrange-3 filter). This is why you can really only use a setting in half-integer sizes. As such, to get a Lagrange order 4 filter you would use the option   -set option:filter:support 2.5

The 'Lagrange' filter was not fully defined and usable until IM version v6.3.7-1.

Cubic Filters

As many image experts were trying to find a better and faster-to-calculate filter for image resizing, a family of filters evolved, and became known as Cubic Filters. These are much like the Lagrange Filters shown previously, and were made up of smaller piece-wise sections. However unlike Lagrange filters the pieces were designed to fit together to form a smooth curve, to reduce sharp blocking effects.

Shown in the graph left are four such 'smooth' cubic filters that are pre-defined within IM, and well known for use as resize filters.

The 'Cubic' filter emulates a Gaussian Blurring Filter. This curve is also known as a 'B-Spline' interpolation curve, and is also used if drawing lines and animated motions of objects in time.

Also shown is 'Catrom', or more correctly the 'Catmull-Rom Filter' or 'Keys Cubic Convolution' which generates a smooth non-blurring form of Interpolation Filter, as well as the 'Hermite' Interpolation Filter.

But there were many other families of Cubic Filters that were being proposed by various experts, to try to reduce the Resize Artifacts that were being seen in images. For example there is the whole 'B-Spline' family of cubics providing various degrees of filtering between blurring ('Cubic') and blocking ('Hermite').

Then there was the 'Cardinal' family, which produces filters compromising between blocking and ringing artifacts and from which the 'Catmull-Rom Filter' ('Catrom') evolved as a balanced compromise of these artifacts.

These two families then merged to try to find an even better balance using 'Keys' filters, which linked the 'Catmull-Rom Filter' (Keys α = 0.5) with the 'B-Spline Cubic Filter' (Keys α = 0.0).

Confused? Of course you are. So was everyone else!

The problem is that the results of filters are often very subjective, dependant on the image, and the restrictions of the 'family' you are using.

Mitchell-Netravali Filter

Into this, Don P. Mitchell and Arun N. Netravali, came out with a paper, 'Reconstruction Filters in Computer Graphics', which formulated two variables known as 'B' (as used for 'B-spline' curves) and 'C' (as used for the 'Cardinal' curves, equivalent to the 'Keys' filter α value) from which you can generate any smoothly fitting piece-wise cubic filter.

Furthermore they then surveyed a group of 9 image processing experts, to classify the Artifacts produced by enlarged images using various values. The results of that survey are shown in the diagram to the right. The 'green' area represented values the experts regarded as producing an acceptable result, while the various other areas produce the various types of Artifacts.

This image is important, as it really shows the relationships between the various Artifacts and the various different types of filters.

You can also see from the results why the 'Keys' family of filters became so important, as one of the better methods of generating good cubic filters. Its filters basically fell in a line directly through the area regarded as 'acceptable' by image processing experts.

From this survey, Mitchell and Netravali determined that the best filter was a 'Keys' family filter, which fell in the middle of the acceptable area, using B,C values of 1/3,1/3. This filter is now known as the 'Mitchell-Netravali Filter' and is available in IM as the 'Mitchell' filter setting. Basically it is a compromise of the acceptable effects of the resize artifacts. It is also the default filter used for IM image enlargements.

ASIDE: As a personal thought, I wonder if anyone looked into using a negative-blur, to try and produce a sharper image? The 'Keys' filter family for example continues into the area below the diagram, though Mitchell and Netravali did not seem to look into it.

All four of the IM Cubic filters: 'Mitchell', 'Catrom', 'Cubic', and 'Hermite'; have also been marked in the above diagram, showing what those experts thought of those specific filters. Also shown are the lines representing the 'B-Spline', 'Cardinal' and 'Keys' filter familys.

Internally all these filters only differ by the pre-defined B,C settings of the filter, in fact IM uses the same library function to generat all cubic filters, only with different B,C settings, for those filters.

Cubic B,C Expert Settings

As of IM v6.3.6-3 you can use the special expert settings to set the B,C settings that a Cubic Filter is using. To do this you need to select any one of the four Cubic filters, and the desired 'b' and 'c' expert settings. For example...

   -filter Cubic
   -set option:filter:b value
   -set option:filter:c value

The expert settings will override the internal defaults for the given filter when it is used by the resizing operator. As such the order of the options does not matter, as long as they have all been set, after the images has been read in (as "-set" is a image meta-data setting operator) and before the resizing operator is used.

To the right is a table of the B,C values for the four defined Cubic Filters. As of IM v6.5.4-1, if one of the 'b' or 'c' settings is not "-set" its value will be calculated from the other value on the assumption that you are wanting a 'Keys' family filter (along the dotted line in the Mitchell-Netravali Survey diagram). Remember the 'c' is equivalent to the Keys α setting, while 'b' can be thought of as a cubic filter 'blur' setting. Remember 'Hermite' is a simple cubic interpolation filter, and not part of the 'Keys' filter family.

Filter B  blur  C Keys α Mitchell 1/3 1/3 Catrom 0.0 1/2 Cubic 1.0 0.0 Hermite 0.0 0.0

The 'Parzen' windowing filter also uses the 'Cubic' filter as its windowing function. As such you can re-define this windowing filter in terms of B,C expert options. You may however have to adjust the 'win-support' expert option if you plan to do this. How useful this is, and what effect it has on the resulting windowed Sinc, is unknown, and not recommended.

Expert Filter Options

In the various sections above I introduce a large number of special 'expert' options which will allow you to control the various filters, using "-set".

Now while they are available. I would like to make one final point.

They are for people who are expert at image processing, and unless you know exactly what they do and how it effects the filtering operation, all you are likely is to destroy the effectiveness of a image processing operator.

Here is a quick list of all expert settings (use at own risk)...

-set option:filter:filter  {filter-type}
   Expert Only
   Override the main filter weighting function being used
   If no 'option:filter:window' is set a 'Box' windowing function will
   also be automatically set, to provide 'raw' access to the filter
   function you have specified.

   For example a 'Raw 8 lobed Sinc' filter can be set using...
     -set option:filter:filter  Sinc
     -set option:filter:lobes   8

   To use the Blackman windowing function directly as a filter
   (just as IM did by mistake, before v6.3.6-3)
     -set option:filter:filter  Blackman
     -set option:filter:support 4.0

-set option:filter:window  {filter-type}
   Expert Only
   Use this filter as the windowing function for Sinc/Bessel,
   The filter weighting function is automatically set to Sinc/Bessel
   unless a specific 'option:filter:filter' has also been set (see above).

   For example a 'Lanczos' filter (Sinc-Windowed-Sinc) is really...
     -set option:filter:window Sinc
     -set option:filter:lobes  3
   Note the 'filter' function defaults to 'Sinc' for orthogonal resizes.

-set option:filter:blur  {multiplier}
   Multiply the support windows and filter function by this multiplier
   Less than 1.0 shrinks filter, usually (not always) making it sharper
   while more than 1.0 will makes a more blurry filter.
   It is best to limit its use to Interpolated or Gaussian-like filters.

   For example a 'less blurry' Gaussian-like filter...
     -filter Cubic
     -set option:filter:blur  0.7

-set option:filter:lobes  {integer}
   Set an integer 'support' size for the current filter, except when used from
   a cylindrical or radially filtering operator (like "-distort"), which uses a
   Windowed-Bessel filter!  In that case the correct support for that many
   Jinc() function 'lobes' is looked up from a internal table of the first 20
   (zero crossing settings).

   For example an 8 lobed 'Lanczos' filter...
     -filter Lanczos
     -set option:filter:lobes   8

   For example an Lagrange-5 filter (lagrange order = support*2-1)
    -filter Lagrange
    -set option:filter:lobes 3

   NB: The 'Lagrange' filter order is automatically adjusted to fit the
   'lobes' or 'support' window. See formula above.

-set option:filter:support {float}
   Force the filter to be clipped to just this support window range.
   Any setting less than 0.5 forces the use of 'Point' filter.

   For example an Lagrange-4 filter
    -filter Lagrange
    -set option:filter:support 2.5

-set option:filter:win-support {float}
   Expert Only
   A method of improve the speed of large support windowed filters.

   If unset it defaults to the appropriate 'support' setting.  If set the
   windowing function will use this support size rather than the normal
   support window, without adjusting the windowing function's effect on the
   filter function.  This can be used to create a large window function
   support, but then clip it to a much smaller actual processing support size,
   to remove the low contribution 'tail' end of the resulting windowed-sinc
   function.

   For example a 12 lobed 'Lanczos' windowed filter clipped to just
   the first 8 lobes of the resulting windowed filter...
      -filter Lanczos
      -set option:filter:win-support 12
      -set option:filter:lobes       8
   This makes it about four times faster, by ignoring the 'tail' of the
   resulting windowed-sinc filter but may have some minor artifacts as a
   result.

-set option:filter:b {float}
-set option:filter:c {float}
   Expert Only
   Override the preset B,C values for a given cubic type of filter.
   If only one option is set the other is calculated to produce a 'Keys'
   filter.   B = B-Spline 'blurriness'  C = Cardinal or Keys α value

   For example, Try a different filter from the 'Mitchell-Netravali' survey.
     -filter Cubic
     -set option:filter:b .5
     -set option:filter:c .5

   Or specify a 'keys' filter according to the Keys α value...
     -filter Cubic
     -set option:filter:c .4

-set option:filter:verbose 1
   Debugging/Graphing Only
   If set (any value), a table of the values of the resulting filter is output
   to standard output from 0.0 to support in .01 increments. This data is
   what was used to generate the graphs shown above for specific filters,
   after all the factors have been calculated and adjusted.  A comment is also
   included indicating the actual 'support window' of the filter, after any
   adjustment by the 'filter:blur' expert setting.

   Extract the data of a Welsh Windowed Sinc Filter...
       convert xc: -filter Welsh -set option:filter:verbose 1 \
               -resize 200% null:  > filter_welsh.dat
   Or the raw Welsh Windowing Function  that the above used
       convert xc: -set option:filter:filter Box \
                   -set option:filter:window Welsh \
                   -set option:filter:support 1.0 \
                   -set option:filter:verbose 1 \
               -resize 200% null:  > window_welsh.dat
   and plot it...
        gnuplot
           plot "window_welsh.dat" using lines

Any use of the expert options are at your own risk. They are not meant for production use, but as a method for exploring or producing tricky or otherwise impossible resize functions. Use at your own peril!

The "-filter" setting is in fact only used to lookup and set the appropriate settings to define the given 'named' resize filter. The above settings will then override those default values as appropriate, at the time a resize operator sets up the filter for a specific resize or distortion image processing operation.

The "-filter" setting 'Point' completely bypasses all the above filter setup, and degenerates into an unscaled 'Nearest Neighbor' direct lookup (resize), or interpolated lookup (distort). Thus this named filter setting effectively turns off the scaled image lookup filter completely.

The 'b' and 'c' expert filter settings only work when a cubic filter is in use, which includes the 'Parzen' cubic windowing function. It is not posible to use a cubic 'Parzen' window function with a 'Cubic' filter weighting function, as this will result in the same cubic curve. Not that cubic windowed cubic filters make any real sense.

Summary of Resize Filters

The following is my own personal view after studying, recoding, and documenting all the above filters available in ImageMagick. If you think I may be wrong or like to express your opinion, I invite you to express your views on the IM forum, and invite me to respond.

Interpolation Filters, such as 'Hermite', are ideal when greatly enlarging images, producing a minimum of blur in the final result, though the output could often be artificially sharpened more in post-processing.

Gaussian-like Blurring Filters, such as 'Mitchell', work best for images which basically consist of line drawings and cartoon like images. You can control the blurring versus the aliasing effects of the filter on the image using the special Filter Blur Setting.

Windowed Sinc/Bessel Filters, and the Lagrange equivalent are the best filters to use with real-world images, and especially when shrinking images. All of them are very similar in basic results. A larger support, or better still, lobe count setting, will generally produce an even better result, though you may get more ringing effects as well, but at a higher calculation cost.

The Cubic Filters are a mixed bag of fast and simple filters, of fixed support (usually 2.0) which produces everything from the 'Hermite' smooth interpolation filter, the qualitatively assessed 'Mitchell' for image enlargements, the very blurry Gaussian-like 'Cubic' filter, or a sharp, windowed-sinc type of filter using 'Catrom'.

Generally if the resize results are acceptable as is, leave things alone, as you are more likely to make things worse, not better.

---

Filter Comparison

Enlargement

To give a final comparison, here I present a selection of 12 representative resize filters. The image is an enlargement of an aliased step in a one pixel wide line, on a dark gray background. The original image itself 'aliased' so you should be able to see how well various filters remove any existing aliasing effects.

The above starts with the Interpolated Filters and continues with the Gaussian Blurring Filters, showing how much blurring these filters produce, and in doing so remove the 'aliasing' in the original image. No ringing is produced by these filters.

The second line starts with three Windowed Sinc Filters showing the heavy ringing effects they can produce. Remember these filters are really designed for shrinking images, not enlarging. This continues into the 'Lagrange' using its default 'interpolating' setting, and 'Catrom' interpolated cubic filter.

The final image is the 'Mitchell' filter showing what the 'experts' subjectively agreed was the best 'ideal' filter to use for enlarging images, with a minimal of all four Resize Artifacts present in the final result.

I myself agree with their findings, but only for enlargements.

This is why 'Mitchell' is the default 'enlargement' filter used by IM.

Shrinking

To get an idea of the aliasing effects, here I shrink the crop of Large Rings Image that we made earlier (105x105 pixels in size), to see what sort of Moiré effects each of the same 12 representative filters generates.

As you can see Interpolated Filters filters are very heavily aliased. On the other hand the blurring effects of the Gaussian Blurring Filters tend to remove the Moiré effects from the resulting image, though with a general blurring of the resulting image. The 'Gaussian' Filter itself does show a barely noticeable aliasing effect caused by its infinite (IIR) filter being clipped by the Filter Support Setting (at a size of 1.5), but that is very minor.

On the other hand the Windowed Sinc Filters produce a very sharp looking image with only a very light circular morié effect, of about equal intensity across all three representative filters. This is probably a 'display' effect rather than a resize filter effect.

Finally the other cubic filters also show some morié effects, with the 'Mitchell' showing the least effect, presumably because of the slight blurring that it has incorporated into the filter.

Here is another comparison, but this time heavily shrinking a Smaller Rings Image smaller.

As you can see the Interpolated Filters produce lots of aliasing artifacts, while the Gaussian Blurring Filters tend to blur out more lines than the others. But all the other filters tend to produce a reasonable job.

The Best Filter?

That is something you will need to work out yourself. Often however it depends on what type of image and resizing you are doing.

For enlarging images 'Mitchell' is probably about the best filter you can use, while basically any of the Windowed Filters (default is 'Lanczos') are good for shrinking images, especially when some type of low level pattern is involved. However if you have no patterns, but lots of straight edges (such as GIF transparency), you may be better off using sharpened Gaussian Filter or again a 'Mitchell', so as to avoid strong ringing effects.

The 'Lagrange' filter is also quite good especially with a larger Filter Support Setting, for shrinking images.

The choice is yours, and choice is a key feature of ImageMagick.

IM's Defaults... It is for these reasons that 'Mitchell' is the default filter for enlargement, and for shrinking images involving transparency. However the 'Lanczos' will be used for shrinking when no-transparency is involved.

---

Created: 15 March 2004
Updated: 12 June 2009
Author: Anthony Thyssen, <A.Thyssen@griffith.edu.au>
Examples Generated with:
URL: http://www.imagemagick.org/Usage/resize/