Image recoloring for Red-Green dichromats with compensation range-based naturalness preservation and refined dichromacy gamut

Wangkang Huang, Zhenyang Zhu, Ligeng Chen, Kentaro Go, Xiaodiao Chen, Xiaoyang Mao

Abstract: People with color vision deficiency (CVD) have difficulty discriminating colors, which can cause loss of chromatic contrast in the perception of affected individuals. To compensate for contrast loss, image recoloring approaches have been proposed in the existing studies. In state-of-the-art studies, recoloring models were built based on an approximated gamut of CVD in the CIE L*a*b* (Lab) color space, which significantly deviates from the original gamut. In addition, luminance was not considered during recoloring. Moreover, existing methods also present problems, such as high computational costs and insufficient naturalness preservation . In this paper, we propose a novel recoloring method to compensate for CVD that enhances contrast through adopting a luminance channel-considered optimization model while preserving naturalness by imposing hard constraints on the amount of changes to the original colors. To obtain a better compensation effect, we fit a new curved surface for representing the gamut of dichromacy in the Lab color space more accurately. Furthermore, a discrete solver is implemented to solve the optimization problem efficiently. For effective assessment, qualitative, quantitative, and subjective experiments were conducted, and a new metric, called preference, is proposed to evaluate the contrast enhancement and naturalness preservation comprehensively.