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PFF-RVM: A new no reference image quality measure

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  • Sadou, Besma
  • Lahoulou, Atidel
  • Bouden, Toufik

Abstract

In this paper, a new no-reference image quality metric is proposed and developed. A set of ten perceptual features are extracted from each distorted image, then Relevance Vector Machine (RVM) is employed to learn the mapping between the combined features and human opinion scores. Validation tests and simulations are conducted on the LIVE II and MDID 2013 databases. The predictive performances of this new metric (we called PFF-RVM for perceptual features fusion using relevance vector machine based metric) are compared to the most recent no-reference metrics in terms of correlation and monotonicity. Results show that the proposed metric has satisfactory and comparable performances to the most sophisticated and commonly used no-reference quality metrics in the state-of-the-art.

Suggested Citation

  • Sadou, Besma & Lahoulou, Atidel & Bouden, Toufik, 2020. "PFF-RVM: A new no reference image quality measure," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 167(C), pages 404-414.
    • Handle: RePEc:eee:matcom:v:167:y:2020:i:c:p:404-414
    DOI: 10.1016/j.matcom.2018.11.005
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