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Vol. 6 No. 1 (2025)
2025 Edition

Articles

Improved No-Reference Image Quality Assessment Algorithm Based on Visual Perception Characteristics

Published 2025-04-14

  • Xu Wang
    • ,
  • Zhong Chu
    • ,
  • Guifan Weng

Xu Wang
Computer Science, Beijing University of Posts and Telecommunications, Beijing, China

Zhong Chu
Information science, Trine University, CA, USA

Guifan Weng
Computer Science, University of Southern California, CA, USA

Abstract

Image quality assessment plays a crucial role in various computer vision applications, yet current no-reference methods often fail to align with human visual perception. This paper presents an enhanced no-reference image quality assessment algorithm that incorporates visual perception characteristics to improve evaluation accuracy. The proposed approach integrates multi-scale gradient analysis, visual saliency mapping, and texture characterization techniques to extract perceptually relevant features. A novel quality score fusion strategy employs machine learning-based regression to optimize feature weighting and parameter adaptation across different image categories. Experimental validation on standard datasets including LIVE, TID2013, and KADID-10k demonstrates significant performance improvements, achieving Spearman rank correlation coefficients of 0.924, 0.891, and 0.887 respectively. The algorithm exhibits superior robustness across various distortion types while maintaining computational efficiency suitable for real-time applications. Comparative analysis with state-of-the-art methods reveals consistent performance gains of 8-15% in correlation metrics, establishing the effectiveness of incorporating visual perception principles in blind image quality assessment frameworks.

Keywords

No-reference image quality assessment, Visual perception, Feature extraction, Quality evaluation

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