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

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

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
JournalVisual Computer
DOIs
Publication statusPublished - 2019 Jan 1

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Keywords

  • Contrast
  • Naturalness
  • Recoloring
  • Red–green dichromacy
  • Subjective evaluation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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