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September 16, 2025Journal of ImagingOpen Access

Research Progress on Color Image Quality Assessment

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Authors

MGMinjuan GaoCSChenye SongQZQiaorong Zhang

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Overview

This review demonstrates the applications and methodologies of color image quality assessment metrics, indicating ongoing challenges and future directions.

Key Points

  • Color image quality assessment (CIQA) uses algorithms to compare objective outputs with subjective perceptions, enhancing image processing techniques.
  • Key categories of CIQA methods include full-reference, reduced-reference, and no-reference approaches, with each having unique applications and implementations.
  • A newly developed CIQA framework evaluates color images, employing machine learning and perception models to advance image quality outcomes.
  • Despite progress in CIQA, challenges persist in adaptability across domains and ensuring contextualized image assessments for varied environments.

Cite This Study

Gao et al. (2025) studied this question.

synapsesocial.com/papers/68d4565431b076d99fa5ad5chttps://doi.org/10.3390/jimaging11090307
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1S-IQA Image Quality Assessment With Compressive Sampling2024
  2. 2A Novel Image Quality Assessment With Globally and Locally Consilient Visual Quality Perception2016 · 109 citations
  3. 3FR-IQA: Joint Perceptual Degradation in Collaborative Feature Refinement2026
  4. 4A study of why we need to reassess full reference image quality assessment with medical images2024
  5. 5A Style Transfer-Based Fast Image Quality Assessment Method for Image Sensors2025