Image quality assessment based on multi-order local features description, modeling and quantification

Research output: Contribution to journalArticle


Image quality assessment (IQA) plays an important role in quality monitoring, evaluation and optimization for image processing systems. However, current quality-Aware feature extraction methods for IQA can hardly balance accuracy and complexity. This paper introduces multi-order local description into image quality assessment for feature extraction. The first-order structure derivative and high-order discriminative information are integrated into local pattern representation to serve as the quality-Aware features. Then joint distributions of the local pattern representation are modeled by spatially enhanced histogram. Finally, the image quality degradation is estimated by quantifying the divergence between such distributions of the reference image and those of the distorted image. Experimental results demonstrate that the proposed method outperforms other state-of-The-Art approaches in consideration of not only accuracy that is consistent with human subjective evaluation, but also robustness and stability across different distortion types and various public databases. It provides a promising choice for image quality assessment development.


  • Yong Ding
  • Xinyu Zhao
  • Zhi Zhang
  • Hang Dai
External organisations
  • Zhejiang University
  • University of York
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Signal Processing


  • Feature extraction, Image quality assessment, Image quality degradation, Local pattern representation, Visual perception
Original languageEnglish
Pages (from-to)1303-1315
Number of pages13
JournalIEICE Transactions on Information and Systems
Issue number6
Publication statusPublished - 2017 Jun 1
Publication categoryResearch