Processing images for red–green dichromats compensation via naturalness and information-preservation considered recoloring

Abstract

Color vision deficiency (CVD) is caused by anomalies in the cone cells of the human retina. It affects approximately 200 million individuals throughout the world. Although previous studies have proposed compensation methods, contrast and naturalness preservation have not been adequately and simultaneously addressed in the state-of-the-art studies. This paper focuses on red-green dichromats' compensation and proposes a recoloring algorithm that combines contrast enhancement and naturalness preservation in a unified optimization model. In this implementation, representative color extraction and edit propagation methods are introduced to maintain global and local information in the recolored image. The quantitative evaluation results showed that the proposed method is competitive with state-of-the-art methods. A subjective experiment was also conducted and the evaluation results revealed that the proposed method obtained the best scores in preserving both naturalness and information for individuals with severe red-green CVD.

Personalized Image Recoloring for Color Vision Deficiency Compensation

“…The recoloring result (Fig. 1(b)top) of Zhu et al [16] preserves contrast and naturalness well for protan 100% (Fig. 1(b)-bottom), but for protan 60%, the tints of the simulation image (Fig.…”

“…(a) and (d): the original images. (b): recoloring result of Zhu et al [16]. (e): recoloring result of Zhu et al [17].…”

“…Considering the large population of people with CVD, adapting the visual contents to perception characteristics of users with CVD is necessary [2]. Several image recoloring methods [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21] have been proposed to compensate for the loss in the ability to discriminate colors. In addition to contrast, the importance of naturalness preservation has also been raised in state-of-the-art image recoloring studies [9], [10], [11], [12], [13], [14], [15], [16], [17].…”

“…Fig. 1 shows two examples of such failures with the two most recent recoloring algorithms [16], [17].…”

“…1. Comparison of the images produced by Zhu et al [16], [17] and the proposed method for assisting people with different degrees of CVD. (a) and (d): the original images.…”