Path: utzoo!attcan!uunet!husc6!think!ames!elroy!usc!venera.isi.edu!raveling
From: raveling@venera.isi.edu (Paul Raveling)
Newsgroups: comp.graphics
Subject: Re: Color Quantization
Message-ID: <8553@venera.isi.edu>
Date: 1 Jun 89 23:12:30 GMT
References: <8490@venera.isi.edu> <3800@eos.UUCP>
Reply-To: raveling@venera.isi.edu (Paul Raveling)
Organization: Information Sciences Institute, Univ. of So. California
Lines: 60

In article <3800@eos.UUCP> jbm@eos.UUCP (Jeffrey Mulligan) writes:
>From article <8490@venera.isi.edu>, by raveling@venera.isi.edu (Paul Raveling):
>
>> 	There are also some applications of quantization that would
>> 	need an approach not based on human perception.  I'm thinking
>> 	of things such as multispectral imaging, including infrared,
>> 	UV, X-ray, and magnetic resonance imaging.  Some applications
>> 	might call for a "linear" quantization, some might benefit
>> 	by weighting for a "non-human" domain.
>
>I'm not sure what you have in mind here.  With these new imaging
>techniques, it would seem to me that you can divide the problem
>into two parts:  first, what are the features that you want to
>detect?  and second, how can you process the image to make those
>features most visible to a human observer while minimizing the
>noise or number of false targets?  The first part doesn't have
>anything to do with vision, while the second part doesn't have
>anything to do with anything except vision.  What is the "non-human"
>domain you are thinking of?

	Let me illustrate with a couple hypothetical examples,
	rather than real ones, since I'm not directly involved
	with these application domains.

	One is medical image fusion.  Take a CAT scan (X-Ray)
	image and a matching MRI scan image of the same cross-section
	of the same patient; preprocess them to match the images,
	then treat each image as a color component of a single fused
	image.  I believe the X-Ray component excels at showing bone,
	but the MRI component is much better for resolving soft tissue.
	Combined, they offer the possibility of showing features not
	easily visible on either scan alone.

	Now quantize the fused image in this artificial color space.
	Quantization can produce various effects either deliberately
	or accidentally (e.g., banding) that could be desirable in
	this domain.  For example, careful tuning could produce
	banding that enhances contrast for some of those fused features
	that are difficult to identify in the individual scans.

	The resulting image would be viewed in RGB space, but the
	colors represent a synthesized graphic domain with no relation
	to human perception.


	A second example might be to use similar fusion techniques
	with satellite images.  For example, red might represent
	a radar image, green an infrared image, and blue a visual
	image.  Instead of diagnosing diseased tissue, the goal
	might be something like correlating air pollution sources
	with environmental damage.

	Tuned quantization probably is useful as an additional
	tool for image processing, but to my knowledge noone
	has yet explored this possibility.


----------------
Paul Raveling
Raveling@isi.edu