# Lanczos resampling

Lanczos resampling

Lanczos resampling ("Lanzosh") is a multivariate interpolation method used to compute new values for any digitally sampled data. It is often used to resize digital images, but could be used for any other digital signal. In the case of digital image resizing, the Lanczos function indicates which pixels in the original image, and in what proportion, make up each pixel of the final image.

Lanczos filtering gives very high quality results compared to more commonly used but faster techniques such as linear or cubic interpolation because it more closely approximates the optimal resampling filter, the sinc function.

static void Test(const double* signal, size_t origlen, size_t newlen){ std::vector newsig(newlen); Resample(signal, origlen, &newsig [0] , newlen); size_t maxlen = std::max(origlen, newlen); std::printf("Original signal: "); size_t bpos = 0; for (size_t a=0; aint main(void){ /* Test resampling into a smaller size */ double smp1 [] = { 0,9,0,0,9,0,0,0,9,0,0,0,9,9,9,9,9,9,9 }; Test(smp1, sizeof(smp1)/sizeof(*smp1), 6); /* Test resampling into a larger size */ double smp2 [] = {9,0,3,0,9,6,9}; Test(smp2, sizeof(smp2)/sizeof(*smp2), 26);}

Example output:

Original signal: 0 90 0 0 90 0 0 0 90 0 0 0 90 90 90 90 90 90 90Resampled signal: 33 .. .. 24 .. .. .. 20 .. .. 25 .. .. .. 91 .. .. .. 89-----Original signal: 90 .. .. .. 0 .. .. .. 30 .. .. .. 0 .. .. .. 90 .. .. .. 60 .. .. .. .. 90Resampled signal:107 95 77 52 23 1 -3 9 25 29 19 4 -2 7 33 65 87 92 83 68 59 63 74 84 92 96-----

As can be observed from the results, the values given by the algorithm are not exact copies of the source values ― they even extend beyond the original range of the samples ― but the general outline is retained when resampled to smaller size, and predicted when resampled to larged size. Among many uses, the algorithm can be applied to audio signal and image data.

Two-dimensional lanczos resampling of images can be performed by first scaling each scanline horizontally, then scaling each resulting column vertically. Such action can be optimized by handling each scanline/column in the inner loop, reusing the same contribution array.

Applications

The Lanczos resampling kernel is known to be used in:Fact|date=September 2008
* FastStone Image Viewer
* [http://freeimage.sourceforge.net FreeImage] (Open Source image encoding/decoding library)
* IrfanView
* AviSynth
* ffdshow
* ImageMagick
* MPlayer
* Shake (software)
* Mac OS X - Quartz (graphics layer) and vImage
* Avidemux
* Lightwave
* Pixel image editor (1.0 Beta 8 and above)
* GIMP (2.3 and above)
* Panorama Tools (as "sinc interpolation")
* XnView
* Opera (web browser) - for zooming images
* Python Imaging Library (as 'ANTIALIAS' in Image.resize())
* ACDSee
* XBMC (as "sinc" and "Lanczos" for video scaling)

ee also

*Bicubic interpolation
*Bilinear interpolation
*Spline interpolation
*Nearest-neighbor interpolation

* [http://home.no.net/dmaurer/~dersch/interpolator/interpolator.html Testing Interpolator Quality]
* [http://www.worldserver.com/turk/computergraphics/ResamplingFilters.pdf PDF from Apple Computer]
* [http://www.worldserver.com/turk/computergraphics/ResamplingKernels.html Resampling Kernels]
* [http://www.antigrain.com/demo/index.html Anti-Grain Geometry examples] image_transforms.cpp shows comparisons of repeatedly resampling an image with various kernels.

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