Lanczos Radius
This is a EWA Lanczos filter that is blurred (sharpened) so that the number of lobes used (3 by default) fits into a integer support radius. That is a 3 lobe EWA Lanczos (based on a Windowed Jinc) is sharpened to have a support of exactly radius 3.
You can use the Lobes Expert Control, to set the number of lobes as the filter is not using a specifically calculated blur factor, as it is in the previous sharpened filters, but an adjustment based on the known zero crossing of the Jinc function.
proper scaling of the Jinc filter for EWA use
 anthony
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Re: proper scaling of the Jinc filter for EWA use
In IM examples, Filters I said...
Anthony Thyssen  Webmaster for ImageMagick Example Pages
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Re: proper scaling of the Jinc filter for EWA use
Clear enough.anthony wrote:Lanczos Radius
This is a EWA Lanczos filter that is blurred (sharpened) so that the number of lobes used (3 by default) fits into a integer support radius. That is a 3 lobe EWA Lanczos (based on a Windowed Jinc) is sharpened to have a support of exactly radius 3.
You can use the Lobes Expert Control, to set the number of lobes as the filter is not using a specifically calculated blur factor, as it is in the previous sharpened filters, but an adjustment based on the known zero crossing of the Jinc function.

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Re: proper scaling of the Jinc filter for EWA use
I don't exactly know what this last post was about, but I'd like to mention that it appears that 4 lobes, with Bessel Jinc as filter, appears slightly inferior to 3 or 5, at least when the windowing is far from being an approximate Gaussian (like Parzen, Quadratic, etc). (Of course this is when using EWA (distort).)
I don't have a very convincing explanation for this. There seems to be something going on with weights having the "right" opposite signs near the edges when using odd numbers of lobes with Jinc. But I have other things to do than dive into this issue, esp. since I'm happy with 3 lobes.
I don't have a very convincing explanation for this. There seems to be something going on with weights having the "right" opposite signs near the edges when using odd numbers of lobes with Jinc. But I have other things to do than dive into this issue, esp. since I'm happy with 3 lobes.
 anthony
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Re: proper scaling of the Jinc filter for EWA use
Note that sinc when used in EWA have very different effects for odd lobes to even lobes. (I forget which was very bad)
Of course using Sinc in a 2Dimensional EWA filter was never a good solution as it can generate zero weight sums, but the same odd/even effect may be what is happening in Jinc.
Of course using Sinc in a 2Dimensional EWA filter was never a good solution as it can generate zero weight sums, but the same odd/even effect may be what is happening in Jinc.
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Re: proper scaling of the Jinc filter for EWA use
A really interesting quote from an expert poster on the madVR forums. The poster, alias 6233638, is almost invariably interesting.
Within madVR, a video renderer, "Jinc 3 AR" refers to (plain vanilla, not the newer one I recommend) EWA LanczosSharp with a proprietary and closed source Anti Ringing filter which was designed by Mathias Rauen. (Mathias has given some idea of how it works, without giving details.)
This makes me wonder if I did not get LanczosSharp just right from the get go, and my preference for a very slightly milder deblur than what is built into IM is not quite as good as I think?
I have good theoretical reasons to prefer the "nonstandard one". But maybe something is going on?
This being said, I explicitly told Mathias that the way I understand his antiring filter to work, he'd be better off with the "plain vanilla LanczosSharp". The experiment of 6233638 does suggest that this piece of advice was right, even though the deblur recommended in http://www.imagemagick.org/Usage/filter ... upsampling may indeed be better when there is no antiring filter of this type at play.

Apologies: No time to fully explain what this is all about. Another day.
(http://forum.doom9.org/showthread.php?p ... ost1614315)I tried taking some photographs to illustrate the differences between various scaling algorithms when displayed at 4:2:2 and 4:4:4, but unfortunately they don't show it as clearly as I had hoped. What I did find though, was that with my TV operating at 4:2:2  at least with the sample that I tried  there is zero difference between Jinc 3 AR and Lanczos 3 AR Chroma. They're not just "very close" or "similar"  I can layer the images on top of each other in Photoshop and use the "difference" layer blending tool, and nothing shows up.
Within madVR, a video renderer, "Jinc 3 AR" refers to (plain vanilla, not the newer one I recommend) EWA LanczosSharp with a proprietary and closed source Anti Ringing filter which was designed by Mathias Rauen. (Mathias has given some idea of how it works, without giving details.)
This makes me wonder if I did not get LanczosSharp just right from the get go, and my preference for a very slightly milder deblur than what is built into IM is not quite as good as I think?
I have good theoretical reasons to prefer the "nonstandard one". But maybe something is going on?
This being said, I explicitly told Mathias that the way I understand his antiring filter to work, he'd be better off with the "plain vanilla LanczosSharp". The experiment of 6233638 does suggest that this piece of advice was right, even though the deblur recommended in http://www.imagemagick.org/Usage/filter ... upsampling may indeed be better when there is no antiring filter of this type at play.

Apologies: No time to fully explain what this is all about. Another day.
Last edited by NicolasRobidoux on 20130215T06:56:1607:00, edited 1 time in total.
 anthony
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Re: proper scaling of the Jinc filter for EWA use
Last few posts split into new separate topic
EWA Filters with a hash pattern
viewtopic.php?f=22&t=22796
EWA Filters with a hash pattern
viewtopic.php?f=22&t=22796
Anthony Thyssen  Webmaster for ImageMagick Example Pages
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Re: proper scaling of the Jinc filter for EWA use
Regarding the "EWA LanczosSharpest" schemes:
I am not 100% sure I computed the deblurs correctly. Will try to triple check in the next little while.NicolasRobidoux wrote:What is interesting although not altogether surprising (if you realize that the goal is to have a Jincwindowed Jinc which with an impulse response as close as possible to a Kronecker delta, and that the outer values are quite small no matter what) is that the (de)blur values for 2, 3 and 4lobes are very close to each other:They are all between 0.8845 and 0.8883, and seem to converge quickly.Code: Select all
convert INPUT.IMG filter Lanczos2 \ define filter:blur=0.88826421508540347 \ distort Resize PERCENT% OUTPUT.IMG convert INPUT.IMG filter Lanczos \ define filter:blur=0.88549061701764 \ distort Resize PERCENT% OUTPUT.IMG convert INPUT.IMG filter Lanczos \ define filter:blur=0.88451002338585141 \ define filter:lobes=4 distort Resize PERCENT% OUTPUT.IMG

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Re: proper scaling of the Jinc filter for EWA use
Prompted by an mpv video player user https://mpv.io/ (and software developer) who found the EWA LanczosSharpest promising and asked about the optimal values for higher number of lobes, I revised the computation of the "optimal" deblurs after finding suspicious stuff in the original versions. Not a bad thing, because I found mistakes: The values I gave for LanczosSharpest4 and LanczosSharpest5 were slightly off. My earlier code nailed the 2lobe deblur value (the LanczosSharpest2 one), but produced values for the 3 and 4lobe blurs which were off by 0.1% and 0.4%, respectively.
With the usual caveat that I redid this rather fast, and that the numerical computations I perform have limited accuracy (when you and I use Bessel functions, we are generally relying on approximations which fall somewhat short of full double precision accuracy), here is the revisited set of blur values, from 2lobes to 8lobes:The corresponding disc radii are (when we deblur, the original disc shrinks):Finally, here is the rather userunfriendly Axiom code that I used to nail the optimal values, and that can be used to check what I did:
With the usual caveat that I redid this rather fast, and that the numerical computations I perform have limited accuracy (when you and I use Bessel functions, we are generally relying on approximations which fall somewhat short of full double precision accuracy), here is the revisited set of blur values, from 2lobes to 8lobes:
Code: Select all
0.8882642150854034, 0.8845100233858514, 0.8880424053585475, 0.8845552452577251, 0.8876859533084833, 0.8851157217316564, 0.88670025197296
Code: Select all
1.983609994601509, 3.7512626128371274, 4.655797087963903, 5.523093681748111, 6.4310715705557175, 7.298128670225191, 8.19833537345964
Code: Select all
 Author: Nicolas Robidoux
 Computation of the deblur of the EWA Lanczos (Jincwindowed
 Jinckernel that makes the convolution, under noop, the closest to
 the identity in infinity linear operator norm. Equivalently, deblur
 that makes the weight of the center pixel equal to 1. Equivalently,
 deblur that makes the sum of the weights of the other pixels equal
 to zero. One may argue that this gives a good approximation of the
 sharpest resampling Jincwindowed Jinc kernel.
 Axiom Computer Algebra System code http://www.axiomdeveloper.org/
 Run with )read pathToThisFile or by cut and pasting into the Axiom terminal window.
)cl a
digits(100)
 Set the number of lobes that the code will check. You may change
 this to any integer between 2 and 8.
numberOfLobes := 2
 Jinc function roots from
 http://cose.math.bas.bg/webMathematica/webComputing/BesselZeros.jsp
 Note: The Bessel function J_1 has root at 0, which Jinc does not,
 so the first root of Jinc is the second root of J_1 etc.
R1 := 3.8317059702075123156144358863081607665645452742878019287622989899188393095190114702141128747574231267244747330972398596077082935393429507463756833072973507947354388612251605091329310067675478669096098
R2 := 7.0155866698156187535370499814765247432763115029113138960553778269854960155020186630727149301794664578066071840984767118820251703769720326548673375642560309232023970190386543841800591275430452193373925
R3 := 13.323691936314223032393684126947876751216644731357865785477571526496567063347304782547119017948839951109445358057000268451481978642541401546368509755789722112502852743021277168603949713032474504688907
R4 := 16.470630050877632812552460470989551449438126822273125769944420007944442567640743899002931497029400154465903980274023428343447658407656485921770613343351595779252911253088717992488254261428837132098213
R5 := 19.615858510468242021125065884137509850247402661880544647351444764470801101727298271485389161157517698967796725446148833952670733337955890767096174193012715265947519553741418696730317913690178285283661
R6 := 22.760084380592771898053005152182257592905370738073226872005077130254273562589664194570326702300201120545899255931397351763974854463339052768274662068684438595699384628576045067718787233238726822557551
R7 := 25.903672087618382625495855445979874287905427031367247641367104494345056714163165058881472268367878871249811307040417420294816882799155844275939749558981998118306731999865474455453558204118663351897861
R8 := 29.046828534916855066647819883531961100414171793083875666040125573376578395536196209367825338086768931611841103790761895902010700540781204477017596137035335307002504273161322353313122231007878096884274
Roots := [ R1, R2, R3, R4, R5, R6, R7, R8 ]
 Select the root that matches the number of lobes.
 The EWA Lanczos (Jincwindowed Jinc) that matches R2 is the two lobe one etc.
Root := Roots.numberOfLobes
Radius := Root / %pi
jinc x == ((besselJ(1,%pi*x))::Float)/x
R1overRadius := R1 / Radius
wind x == (besselJ(1,x*R1overRadius)::Float)/x
l x == if (x<Radius) then ( wind(x) * jinc(x) ) else 0.
z := l 1.e128
 The code assumes that we are deblurring (meaning that we are making the effective radius smaller, not larger).
 As a result, we know that if we are farther away than Radius, the weight is 0.
integerRadius := (floor(Radius)) :: INT
quadrant r == reduce( +, [ reduce( +, [ l(r*sqrt((i^2+j^2)::Float)) for i in 1..integerRadius ] ) for j in 0..integerRadius ] )
residual r == quadrant(r) / ( z + 4. * quadrant(r) )

 Number of lobes = 2:
[ residual(1.12579115877571804+i*.000000000000000001) :: SF for i in 0..10 ]
 Check: Residual at the estimated minimizer
minimizer2 := 1.125791158775718044
residual2 := residual(minimizer2)
 Actual blur value:
blur2 := 1. / minimizer2

 Number of lobes = 3:
[ residual(1.13056943794945355+i*.000000000000000001) :: SF for i in 0..10 ]
minimizer3 := 1.130569437949453554
residual3 := residual(minimizer3)
blur3 := 1 / minimizer3

 Number of lobes = 4:
[ residual(1.12607235191234992+i*.000000000000000001) :: SF for i in 0..10 ]
minimizer4 := 1.126072351912349924
residual4 := residual(minimizer4)
blur4 := 1 / minimizer4

 Number of lobes = 5:
[ residual(1.13051163888428338+i*.000000000000000001) :: SF for i in 0..10 ]
minimizer5 := 1.13051163888428339
residual5 := residual(minimizer5)
blur5 := 1 / minimizer5

 Number of lobes = 6:
[ residual(1.12652452849221331+i*.000000000000000001) :: SF for i in 0..10 ]
minimizer6 := 1.126524528492213318
residual6 := residual(minimizer6)
blur6 := 1 / minimizer6

 Number of lobes = 7:
[ residual(1.12979577183826532+i*.000000000000000001) :: SF for i in 0..10 ]
minimizer7 := 1.129795771838265326
residual7 := residual(minimizer7)
blur7 := 1 / minimizer7

 Number of lobes = 8:
[ residual(1.12777683075531042+i*.000000000000000001) :: SF for i in 0..10 ]
minimizer8 := 1.127776830755310429
residual8 := residual(minimizer8)
blur8 := 1 / minimizer8

[ residual2, residual3, residual4, residual5, residual6, residual7, residual8 ]
%.(numberOfLobes1)
blur := [ blur2, blur3, blur4, blur5, blur6, blur7, blur8 ]
radius := [ blur.i * Roots.(i+1) / %pi for i in 1..7 ]
[ i for i in 2..8 ]
blur :: List SF
radius :: List SF
 Final output:
 Numbers of lobes for which the deblurs were computed:
 [ 2, 3, 4, 5, 6, 7, 8]
 Deblurs in the same order:
 [ 0.8882642150854034, 0.8845100233858514, 0.8880424053585475, 0.8845552452577251, 0.8876859533084833, 0.8851157217316564, 0.88670025197296 ]
 Nominal radii of the support of the corresponding Jincwindowed Jincs:
 [ 1.983609994601509, 3.7512626128371274, 4.655797087963903, 5.523093681748111, 6.4310715705557175, 7.298128670225191, 8.19833537345964 ]
Last edited by NicolasRobidoux on 20151227T02:50:1307:00, edited 6 times in total.

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Re: proper scaling of the Jinc filter for EWA use
Two comments about my last post:
Note how fast the deblurs converge, and the oddeven bracketing. This is unsurprising considering we are fixing things so that the sum of the offcenter coefficients is zero: Slightly changing the windowing and adding more weights near the outer edge of the disc does not change the sum much; also, adding a lobe introduces weights of a fixed sign (negative when the last lobe is even, and positive otherwise), and the outer weights are small compared to the central ones.
It is also interesting that the deblurred radius of LanczosSharpest2 is just under 2, the radius of (cubic) BCsplines (like RobidouxSharp, for example).
Note how fast the deblurs converge, and the oddeven bracketing. This is unsurprising considering we are fixing things so that the sum of the offcenter coefficients is zero: Slightly changing the windowing and adding more weights near the outer edge of the disc does not change the sum much; also, adding a lobe introduces weights of a fixed sign (negative when the last lobe is even, and positive otherwise), and the outer weights are small compared to the central ones.
It is also interesting that the deblurred radius of LanczosSharpest2 is just under 2, the radius of (cubic) BCsplines (like RobidouxSharp, for example).

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Re: proper scaling of the Jinc filter for EWA use
I don't have the time to check, but it would be nice to compare the graph of the weights obtained with LanczosSharpest2 with the graph of those obtained with RobidouxSharp.