In this article the authors show that image quality measures can be successfully used to develop image-individualized gamut mapping algorithms. First the authors compare different image quality measures for the gamut mapping problem and then validate them using psychovisual data from four recent gamut mapping studies. The scoring function used to validate the quality measures is the hit rate, i.e., the percentage of correct choice predictions on data from psychovisual tests. Some of the image quality measures predict the observer's preferences as good as scaling methods such as Thurstones method, which is used to evaluate the psychovisual tests. This is remarkable because the scaling methods are based on the experimental data, whereas the quality measures are independent of these data. The best performing image quality measure is used to automatically select the optimal gamut mapping algorithm for an individual image.