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From: mlm@nl.cs.cmu.edu (Michael L. Mauldin)
Newsgroups: comp.graphics
Subject: Color quantization: flesh tones
Keywords: color rgb quantization flesh tone preferences histograms
Message-ID: <6087@pt.cs.cmu.edu>
Date: 7 Sep 89 16:23:38 GMT
Organization: Carnegie-Mellon University, CS/RI
Lines: 38


Has anyone experimented with psychological color preferences in
quantizing using Heckbert's median cut?  Here's an example problem:

    In some images containing people's faces, where the face is
    only a small part of the image, very few colors are assigned
    to "flesh" color.  The result is banding/loss of resolution in
    an area of the image that is interesting to the viewer out of
    proportion to its relative size.  The problem is most severe
    when quantizing to 32 or fewer colors.

I tried the following experiment, with mixed results.  Choose a color
that is "flesh" (I used <192,96,80>), and after the image has been
histogrammed, but before the median cut color assignment is done,
multiply each cell by a "bonus" between 1 and 2 if it is within some
minimum distance from this point.  On an image of "Our God of Free
Software, RMS", where the face filled about 8% of the screen, using 32
colors, the number of "flesh" colors was increased from 3 to 6, and
significant detail was added to the facial region.  On another image,
a baby picture with significantly "whiter" skin, the method didn't
affect the image much, and when quantizing the RMS image with 16
colors, the whole image tended to look like a sepia tone print, rather
than a color image.

I can think of several modifications:

	1. Get a better definition of "flesh" (racially unbiased :-)
	2. Tweak the bonus function
	3. [Actually used in some Amiga software] extract a subimage
	   containing mostly the feature(s) of interest, build the
	   colormap using statistics form this region.

Anybody else have any good ideas?  Has anyone else experimented with
this?  Is there a reference I don't know about?

Michael L. Mauldin (Fuzzy)		School of Computer Science
ARPA: Michael.Mauldin@NL.CS.CMU.EDU	Carnegie Mellon University
Phone: (412) 268-3065			Pittsburgh, PA  15213-3890