Computational color prediction versus least-dissimilar matching

Emitis Roshan, Brian Funt
2018 Optical Society of America. Journal A: Optics, Image Science, and Vision  
The performance of color prediction methods CIECAM02, KSM 2 , Waypoint, Best Linear, Metamer Mismatch Volume Center, and Relit color signal are compared in terms of how well they explain Logvinenko and Tokunaga's asymmetric color matching results [Seeing Perceiving 24, 407 (2011)]. In their experiment, four observers were asked to determine (three 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
more » ... se 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.
doi:10.1364/josaa.35.00b292 pmid:29603955 fatcat:uxp7zl47wffrzamgzt3gef26ly