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Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
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Research Progress on Color Image Quality Assessment.

Minjuan Gao1, Chenye Song1, Qiaorong Zhang1

  • 1School of Computer Science and Technology, Taiyuan Normal University, Jinzhong 030619, China.

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|September 26, 2025
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Summary
This summary is machine-generated.

This study reviews color image quality assessment (CIQA), exploring its applications and algorithms. It introduces a new CIQA framework, focusing on full-reference and no-reference methods for improved image evaluation.

Keywords:
color image quality assessmentfull-reference methodshuman visual systemno-reference methods

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Area of Science:

  • Computer Vision
  • Image Processing
  • Perceptual Computing

Background:

  • Image quality assessment (IQA) is crucial for evaluating objective algorithms against subjective human perception.
  • Color image quality assessment (CIQA) specifically addresses the complexities of color image evaluation.
  • Existing CIQA methods are categorized into full-reference (FR), reduced-reference (RR), and no-reference (NR) approaches.

Purpose of the Study:

  • To systematically review CIQA applications in image compression, processing optimization, and specialized domains.
  • To analyze benchmark datasets and assessment metrics used in CIQA.
  • To introduce and evaluate a novel CIQA framework for color images, focusing on FR and NR methods.

Main Methods:

  • Categorization of CIQA algorithms into FR, RR, and NR types.
  • Analysis of FR methods using reference images, machine learning, and visual perception models.
  • Development and application of NR methods utilizing distortion-only features via extraction and fusion techniques.
  • Specialized CIQA algorithm development for robotics, low-light, and underwater imaging.

Main Results:

  • The study evaluates color images using a newly developed CIQA framework.
  • FR methods leverage reference images and advanced models for quality assessment.
  • NR methods effectively use distortion features for quality evaluation without reference images.
  • Specialized CIQA algorithms show promise in domain-specific applications.

Conclusions:

  • CIQA is vital for various image applications, with FR and NR methods offering distinct advantages.
  • The developed CIQA framework provides a new approach to evaluating color image quality.
  • Challenges in cross-domain adaptability and generalization persist, highlighting areas for future research.
  • Future work should focus on adaptive, context-aware, and synergistic CIQA approaches.