Abstract

Formal multivariate optimization techniques were applied in an attempt to determine how well a linear, opponent-colors model of color vision could account for specific brightness-matching data. The data fitted were from the Sanders-Wyszecki experiment that matched an adjustable white light in brightness to each of a set of lights of ninety-six different chromaticities and constant luminance. A generalized, linear, opponent-colors model was formulated, which includes the linear models of Guth (and co-workers), Ingling (and co-workers), and Thornton as special cases. The model contained ten parameters, including nine determining the spectral responses of the three opponent-level channels, and one determining the rule for combining the outputs of the three channels to obtain an estimate of equivalent luminance (the luminance of an equally bright white light). Despite difficulties with the optimization procedure, a model was found that correlates better than 0.98 with the fitted data. The predictions of this model for various other color vision functions were explored. These predictions are less than perfect but surprisingly good considering that the model was optimized entirely on brightness data (the only restriction being that the luminance channel should have no negative values).

© 1985 Optical Society of America