As part of a research program to improve the relationship between visual and numerical color‐difference evaluation for industrial colorimetry, a color‐difference tolerance data set for fitting and testing of color‐difference metrics has been extended to include 156 individual color‐tolerance determinations. These tolerances were designed to sample 19 color centers over a surface color gamut with balanced sampling of lightness and chromaticness differences. The tolerance determination procedures emphasized accurate estimation of population visual color‐difference response and rigorous estimation of tolerance precision. Tolerance accuracy was confirmed by excellent agreement of these results and the majority of previous experiments on five color centers selected for CIE color‐difference evaluations. The average uncertainty of the tolerance determinations was ± 11% of the tolerance value at a 2 ó level (95% confidence interval). The completed data set is suitable for estimating the parameters of color‐difference metrics or testing the performance of such metrics. The color tolerances indicated the systematic lack of uniformity of the CIELAB space, in general agreement with previous experiments. A simple modification of the CIELAB color‐difference metric was shown to account for much of the systematic lack of uniformity.

SummaryWhen moderate pain was presented in a context of intense pain, it induced a relief and ‘hedonic flip’ such that pain was reported as pleasant.

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.

No abstract