Computational Ophthalmology – Anomalous Trichromacy Compensation

Personalized Image Recoloring for Color Vision Deficiency Compensation

Zhenyang ZHU, Masahiro TOYOURA, Kentaro GO, Kenji KASHIWAGI, Issei FUJISHIRO, Tien-Tsin Wong,
Xiaoyang MAO

Abstract
Several image recoloring methods have been proposed to compensate for the loss of contrast caused by color vision deficiency (CVD). However, these methods only work for dichromacy (a case in which one of the three types of cone cells loses its function completely), while the majority of CVD is anomalous trichromacy (another case in which one of the three types of cone cells partially loses its function). In this paper, a novel degree-adaptable recoloring algorithm is presented, which recolors images by minimizing an objective function constrained by contrast enhancement and naturalness preservation. To assess the effectiveness of the proposed method, a quantitative evaluation using common metrics and subjective studies involving 14 volunteers with varying degrees of CVD are conducted. The results of the evaluation experiment show that the proposed personalized recoloring method outperforms the state-of-the-art methods, achieving desirable contrast enhancement adapted to different degrees of CVD while preserving naturalness as much as possible.

Links

  1. Publication: IEEE
  2. Source Code: Zip (Matlab)
  3. Manuscript: PDF (Open Access)

Citation

Zhenyang Zhu, Masahiro Toyoura, Kentaro Go, Kenji Kashiwagi, Issei Fujishiro, Tien-Tsin Wong, Xiaoyang Mao, “Personalized Image Recoloring for Color Vision Deficiency Compensation,” IEEE Transactions on Multimedia, First online 2021-03-31.

Acknowledgment

This work was supported by JSPS Grants-in-Aid for Scientific Research, Japan under Grant 17H00738, 20J15406. The authors would like to thank all the participants for evaluating the proposed method carefully and patiently and for their valuable comments. At the same time, we would like to thank the reviewers for their constructive comments and suggestions! We appreciate our project members, especially Mr. Ying Tang for the valuable discussions.