Gamut mapping optimization algorithm based on gamut-mapped image measure (GMIM)

This paper presented a novel gamut mapping optimization algorithm based on a special designed gamut-mapped image measure (GMIM). We formulated GMIM as an objective function in an iterative manner, where the iterative step changes based on the information weight which can be computed between the original image and the initial gamut-mapped images. A new metric, GMIM, is developed to compute the achromatic differences and the chromatic differences of the input images based on structural similarity index. For the achromatic part, we introduce the gradient information to detect the achromatic distortions between two images; the chromatic part aims to analyze the hue and chroma information. Further, the circular statistical theory is employed to calculate the hue value. In the iterative process, we change the iterative steps according to the information weight which can be computed by the information theory. The information map between images indicated regions in an image which human paid attentions to. Experimental results demonstrated that our new gamut mapping method can preserve the brightness, color, as well as detail information of the reference images.