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The potential of image-dependent gamut mapping as a constrained optimization problem is examined. Results suggest that image-dependent gamut mapping gives good mapping results and avoids visual artifacts.

The article describes a robust algorithm for the multitude of images in a typical color management workflow that allows the subsequent improvement of existing global gamut mappings. The spatial methods can be successfully added to existing gamut mapping algorithms with a substantial gain in perceived image quality.

Existing image-difference measures show excellent accuracy in predicting distortions, such as lossy compression, noise, and blur. Their performance on certain other distortions could be improved; one example of this is gamut mapping. This is partly because they either do not interpret chromatic information correctly or they ignore it entirely. We present an image-difference framework that comprises image normalization, feature extraction, and feature combination. Based on this framework, we create image-difference measures by selecting specific implementations for each of the steps. Particular emphasis is placed on using color information to improve the assessment of gamut-mapped images. Our best image-difference measure shows significantly higher prediction accuracy on a gamut-mapping dataset than all other evaluated measures. [ABSTRACT FROM PUBLISHER]

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]