Color quantization: Difference between revisions

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Revision as of 23:24, 12 August 2011

Color quantization is the process of reducing number of colors used in an image while trying to maintain the visual appearance of the original image. In general, it is a form of cluster analysis, if each RGB color value is considered as a coordinate triple in the 3D colorspace. There are some well know algorithms [[1]], each with its own advantages and drawbacks.

Task: Take an RGB color image and reduce its colors to some smaller number (< 256). For this task, use the frog as input and reduce colors to 16, and output the resulting colors. The chosen colors should be adaptive to the input image, meaning you should not use a fixed palette such as Web colors or Windows system palette. Dithering is not required.

Note: the funny color bar on top of the frog image is intentional.

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Here, we use a simplistic averaging technique to build an initial set of colors and then use k-means clustering to refine them.

<lang j>kmc=:4 :0

 K=. x
 img=. y
 C=. 256 #.inv ,img
 G=. K (i.@] <.@* %) #C
 M=. _
 whilst.-. M-:&<.&(k&*)M0 do.
   M0=. M
   M=. /:~C (+/ % #)&.:*:/.~ G
   G=. (i. <./)"1 C +/&.:*: .- |:M
 end.
 M

)</lang>

The left argument is the number of colors desired.

The right argument is the image, with pixels represented as bmp color integers (base 256 numbers).

The result is the colors represented as pixel triples. They are shown here as fractional numbers, but they should be either rounded to the nearest integer in the range 0..255 or scaled so they are floating point triples in the range 0..1.