A new out-of-gamut determination method of image based on irregular segmentation

Abstract

The out-of-gamut determination of images is very important for color gamut mapping which plays an important role in cross-media color reproduction. In this paper, aiming to achieve an accurate out-of-gamut determination of image and exploit the full potential of the reproduction device gamut, we propose a out-of-gamut determination method of images based on irregular segmentation which divides the color gamut into several parts according to the chroma and lightness of colors in CIELAB color space. First, the device color gamut is divided into the high-chroma and the low-chroma color parts, and the high-chroma parts are divided into more segments through the CIELAB a*b* plane when calculating color gamut descriptors. Then, in each segment the radiuses between the color points and the center point are calculated. For the color points located in the high-chroma parts, the color point with the biggest radius is selected as the gamut boundary descriptor, and for that in the low-chroma parts, if the corresponding outer segments are empty, the color point with the biggest radius is selected as the gamut boundary descriptor. Through the irregular segmentation of gamut, the GBDs distribute more uniformly among the color gamut surface than segment maximum method which treats all colors in the same way wherever they are located in the gamut. After that, determination of the out-of-gamut colors can be done by calculating the position relationship between the source colors and the GBDs. Additionally, GMAs would benefit from this accurate out-of-gamut determination of image. GMAs based on this out-of-gamut determination method validate the promising results.