Personalized Image Recoloring for Color Vision Deficiency Compensation

Zhenyang Zhu, Masahiro Toyoura, Kentaro Go, Kenji Kashiwagi, Issei Fujishiro, Tien Tsin Wong, Xiaoyang Mao

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
JournalIEEE Transactions on Multimedia
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Adaptation models
  • Clustering algorithms
  • color vision deficiency
  • Computational modeling
  • contrast enhancement
  • Image color analysis
  • naturalness preservation
  • Optimization
  • recoloring personalization
  • Sensitivity
  • Visualization

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Personalized Image Recoloring for Color Vision Deficiency Compensation'. Together they form a unique fingerprint.

Cite this