mmt@dciem.UUCP (Martin Taylor) (01/28/88)
Alan Paeth responds; >[discussion of blue spatially poor but colorimetrically good resolution >and appropriate dithering techniques] >The real problem here stems from treating the creation of a suitable color >space. The previous posting suggests an opponent space. I've tried Floyd- >Steinberg techniques in this space, as well as LUV, YIQ and HSV with varying >(and noticeably distinct) results. These spaces are largely a change in basis, >or straight-forward non-linear transformation of RGB. As such, they are highly >symmetric, so forcing a degree of high precision to some corner of the space >by bit allocation almost always gives rise to a "conjugate" space of increased >precision in an area which is uninteresting (the precision increase in this >conjugate area is not very perceptual and is thus wasted). Yes, it is very hard to get a uniformly "interesting" space in which to represent the colour of scenes. My experience comes from attempting effecive colouring of Landsat and multi-spectral infrared imagery, which in both cases start with more bands than the eye has colour channels. The natural (to me) process (as of 1974-6) was to do a principal components rotation of the input data to get three maximally informative channels (with various histogram equalization and non-linear compression tricks thrown in), and then to map the three onto the three dimensions of visual space. Naturally, I wanted to equalize the information load on all portions of the space, and the first natural thing to do was to use a UV space in which the macAdam ellipses are more or less equal and circular. The results were TERRIBLE. It took many months before I realized that they were not the whole story, and that most Lab colour-vision experiments do not transfer well to real world scenes. The clue was a 1954 paper by Chapanis and someone in the Optical Society journal, in which the visual scene was not 2 or 4 coloured areas, but 200+. In these spaces, the ellipses are much more evenly balanced across the CIE space (not even, but more so). What I did then was to extrapolate the difference between the UV transformation and the transformation leading to uniform Chapanis and Halsey (I remember now!) diiscrimination. The result was almost equivalent to using an opponents colours (non-linear) transform, and the resulting pictures looked good in the sense of being not only informative, but alos aesthetically pleasing. I did no dithering, as the interesting elements involved high spatial resolution, but I always wanted to get back to the problem and twist the original PC analysis to take account of larger areas for each successive component, thus giving the good blue-yellow large-are discrimination a chance to be useful. As an aside, we had Norpack, a graphics company in Packenham, Ont., make a hardware device for doing arbitrary colour maps such as Alan Paeth described in an earlier posting, on-line at (I think) 20 MHz. We used it to implement both the PC transformation and the colour mapping of Landsat data. One thing it was used for was to create forest fire hazard maps. It was interesting to me to read in IEEE ASSP about 10 years later that my technique had been "discovered" and did good things. The author did not know that it had been in general production use in Canada for a decade! Around the same time, another Canadian company that had developed a producted using the technique had to defend itself against a US patent on the methods. -- Martin Taylor {allegra,linus,ihnp4,floyd,ubc-vision}!utzoo!dciem!mmt {uw-beaver,qucis,watmath}!utcsri!dciem!mmt mmt@zorac.arpa Magic is just advanced technology ... so is intelligence. Before computers, the ability to do arithmetic was proof of intelligence. What proves intelligence now?