Toward Reducing Observer Metamerism in Industrial Applications: Colorimetric Observer Categories and Observer Classification

The variability among color-normal observers poses a challenge to modern display colorimetry because of their peaky primaries. But such devices also hold the key to a future solution to this issue. In this paper, we present a method for deriving seven distinct colorimetric observer categories, and also a method for classifying individual observers as belonging to one of these seven categories. Five representative L, M and S cone fundamentals (a total of 125 combinations) were derived through a cluster analysis on the combined set of 47-observer data from 1959 Stiles-Burch study, and 61 color matching functions derived from the CIE 2006 model corresponding to 20-80 age parameter range. From these, a reduced set of seven representative observers were derived through an iterative algorithm, using several predefined criteria on perceptual color differences (delta E*00) with respect to actual color matching functions of the 47 Stiles-Burch observers, computed for the 240 Colorchecker samples viewed under D65 illumination. Next, an observer classification method was implemented using two displays, one with broad-band primaries and the other with narrow-band primaries. In paired presentations on the two displays, eight color-matches corresponding to the CIE 10° standard observer and the seven observer categories were shown in random sequences. Thirty observers evaluated all eight versions of fifteen test colors. For majority of the observers, only one or two categories consistently produced either acceptable or satisfactory matches for all colors. The CIE 10° standard observer was never selected as the most preferred category for any observer, and for six observers, it was rejected as an unacceptable match for more than 50% of the test colors. The results show that it is possible to effectively classify real, color-normal observers into a small number of categories, which in certain application contexts, can produce perceptibly better color matches for many observers compared to the matches predicted by the CIE 10° standard observer.