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Selecting appropriate gamut mapping algorithms based on a combination of image statistics

Color gamut mapping has been recognized as one of the key elements for accurate image reproduction. A variety of gamut mapping algorithms (GMAs) has been proposed to minimize the image differences between originals and their reproductions across different media. However, the use of a single-GMA is normally unable to fulfill all types of image reproductions. Therefore, we doubt if it’s a right way to further improve the performance of a single algorithm while increasing its complexity. In this study, we propose an optimal multi-GMAs approach based on image analysis to select an appropriate GMA from a range of LUT-type GMAs to give the image the best treatment at low-computational cost. Because there are too many image statistics can be extracted from an image, the present study introduces methods to select the principal statistics for the GMA selection. To save the cost for real-time image analysis, the importance of scanning patterns and image compression was investigated as well. The results suggested that the performance of the above applications would be enhanced to a certain level if we choose appropriate image statistics for calculation.