Computational Color Prediction versus Least-Dissimilar Matching

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Roshan, E., and Funt, B. "Computational Color Prediction versus Least-Dissimilar matching," J. Opt. Soc. Am. A 35, B292-B298 (March 2018).

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The performance of color prediction methods CIECAM02, KSM2, Waypoint, Best Linear, MMV center, and relit color signal are compared in terms of how well they explain Logvinenko & Tokunaga’s asymmetric color matching results (“Colour Constancy as Measured by Least Dissimilar Matching,” Seeing and Perceiving, vol. 24, no. 5, pp. 407- 452, 2011). In their experiment, 4 observers were asked to determine (3 repeats) for a given Munsell paper under a test illuminant which of 22 other Munsell papers was the least-dissimilar under a match illuminant. Their use of “least-dissimilar” as opposed to “matching” is an important aspect of their experiment. Their results raise several questions. Question 1: Are observers choosing the original Munsell paper under the match illuminant? If they are, then the average (over 12 matches) color signal (i.e., cone LMS or CIE XYZ) made under a given illuminant condition should correspond to that of the test paper’s color signal under the match illuminant. Computation shows that the mean color signal of the matched papers is close to the color signal of the physically identical paper under the match illuminant. Question 2: Which color prediction method most closely predicts the observers’ average leastdissimilar match? Question 3: Given the variability between observers, how do individual observers compare to the computational methods in predicting the average observer matches? A leave-one-observer-out comparison shows that individual observers, somewhat surprisingly, predict the average matches of the remaining observers better than any of the above color prediction methods.


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