Image Quality Measures for Evaluating Gamut Mapping

In this paper we compare different image quality measures for the gamut mapping problem, and validate them using psycho-visual data from four recent gamut mapping studies. The psycho-visual data are choice data of the form: given an original image and two images obtained by applying different gamut mapping algorithms, an observer chooses the one that reproduces the original image better in his/her opinion. The scoring function used to validate the quality measures is the hit rate, i.e., the percentage of correct choice predictions on data from the psycho-visual tests. We also propose a new image quality measure based on the difference in color and local contrast. This measure compares well to the measures from the literature on our psycho-visual data. Some of these measures predict the observer's preferences equally well as scaling methods like Thurstone's method or conjoint analysis that are used to evaluate the psycho-visual tests. This is remarkable in the sense that the scaling methods are based on the experimental data, whereas the quality measures are independent of this data.