Performance of a Chromatic Adaptation Transform Based on Spectral Sharpening
The Bradford chromatic adaptation transform, empirically derived by Lam, models illumination change. Specifically, it pro-vides a means of mapping XYZs under a reference light source to XYZs for a target light source such that the corresponding XYZs produce the same perceived color. One implication of the Bradford chromatic adaptation transform is that color correction for illumination takes place not in cone space but rather in a ‘narrowed’ cone space. The Bradford sensors have their sensitivity more narrowly concentrated than the cones. However, Bradford sensors are not optimally narrow. Indeed, recent work has shown that it is possible to sharpen sensors to a much greater extent than Bradford. The focus of this paper is comparing the perceptual error between actual appearance and predicted appearance of a color under different illuminants, since it is perceptual error that the Bradford transform minimizes. Lam’s original experiments are revisited and perceptual per-formance of the Bradford transform and linearized Bradford transform is compared with that of a new adaptation transform that is based on sharp sensors. Perceptual errors in CIELAB delta E, delta ECIE94, and delta ECMC(1:1) are calculated for several corresponding color data sets and analyzed for their statistical significance. The results are found to be similar for the two transforms, with Bradford performing slightly better depending on the data set and color difference metric used. The sharp transform performs equally well as the linearized Bradford transform: there is no statistically significant difference in performance for most data sets.