Graph-based color Gamut Mapping using neighbor metric

Abstract

Colors are displayed in different ways on various devices, such as cameras, screens and printers. In order to achieve consistent appearance on these devices, color management is usually used, where the core part is Gamut Mapping Algorithm (GMA). However, the widely adopted Point-wise Gamut Mapping Algorithms (PGMAs) have been restricted to compromise between color accuracy and details. In this work, we firstly split color space into small cubes through sampling colors from it. Then, we built a 26-neighbored graph with the sample colors as vertexes and perceptual color differences between adjacent vertexes as weights. Based on the above graph, a new Multi-source Shortest Paths Algorithm (MSS-PA) is proposed to establish color mapping relationships between out-of-gamut colors and colors in gamut boundary. In the MSSPA, distance of shortest path between nonadjacent vertexes are used to replace those calculated directly using CIEDE2000, which is useful to measure large color difference. Experimental results show that our method achieves superior performance on the aspect of keeping accuracy and preserving details compared with HPMinDE and SGCK.