Performance testing of color‐difference metrics using a color tolerance dataset

Abstract A color‐difference dataset was developed for testing the performance of color metrics. The dataset comprises 45 color‐difference vectors varying in five directions at nine color centers under conditions typical of commercial color decisions. Probit analysis was used to estimate the parameters of the population distribution of tolerances for each vector. In addition to estimating the median tolerance, the anlysis allows one to estimate the uncertainty of a tolerance and to test the adequacy of the underlying model tolerance distribution. The median tolerances were used to specify 45 color‐difference pairs with equal visual color‐difference magnitudes. The performance of eight color‐difference metrics was compared using the normalized standard deviation of the color differences of the visually equal difference pairs as a measure of uniformity. A bootstrap statistical technique was used to quantify the variation in performance with varying samples of color centers and color‐difference directions and to determine the significance of observed differences in uniformity performance. Some metrics based on weighted CIELAB dl*, dC*, dH* color‐difference components had significantly superior performance compared to the CIE recommended color‐difference metrics.