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We show that conjoint analysis, a popular multi-attribute preference assessment technique used in market research, is a well suited tool to evaluate a multitude of gamut mapping algorithms simultaneously. Our analysis is based on data from psychovisual tests assessed in a laboratory and in a web environment. Conjoint analysis allows us to quantify the contribution of every single parameter value to the perceived value of the algorithm; it also allows us to test the influence of additional parameters like gamut size or color shifts.We show that conjoint analysis can be individualized to images or observers if enough data is available. Especially promising in this respect is the combination of individual and population data. [ABSTRACT FROM AUTHOR]

We explore the potential of image-dependent gamut mapping as a constrained optimization problem. The performance of our new approach is compared to standard reference gamut mapping algorithms in psycho-visual tests. [ABSTRACT FROM AUTHOR]