No-reference image quality assessment for contrast-changed images via a semi-supervised robust PCA model

Jingchao CAO, Ran WANG, Yuheng JIA, Xinfeng ZHANG, Shiqi WANG, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

8 Citations (Scopus)

Abstract

Contrast plays an important role in human perception of image quality. In this paper, we propose a metric for no-reference quality assessment of contrast-changed images by using a novel semi-supervised robust PCA, which can realize feature selection and denoising simultaneously, guided by the available supervisory information. To select features adaptively, the information-oriented features (e.g. entropy and natural scene statistics) and appearance-oriented features (e.g. colorfulness) are adopted. The proposed model is formulated as a constraint optimization problem, which is further casted to a convex problem and solved via augmented Lagrangian multiplier method. Extensive experimental results on CCID2014, CSIQ, SIQAD and TID2013 databases show that the proposed semi-supervised image quality metric based on robust PCA (SIQMR) provides a more accurate prediction than other metrics on the human perception of contrast variations.
Original languageEnglish
Pages (from-to)640-652
JournalInformation Sciences
Volume574
Early online date23 Jul 2021
DOIs
Publication statusPublished - Oct 2021
Externally publishedYes

Bibliographical note

This work was supported in part by the General Research Fund-Research Grants Council (GRF-RGC) under Grant 9042816 (CityU 11209819) and Grant 9042958 (CityU 11203820), in part by the National Natural Science Foundation of China (Grants 61772344 and 61732011), in part by the Natural Science Foundation of Shenzhen (University Stability Support Program No. 20200804193857002), and in part by the Interdisciplinary Innovation Team of Shenzhen University.

Keywords

  • Contrast change
  • Denoising
  • Feature selection
  • Image quality assessment
  • Robust PCA

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