Contrastive distortion-level learning-based no-reference image-quality assessment

Xuekai WEI, Jing LI*, Mingliang ZHOU, Xianmin WANG

*Corresponding author for this work

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

8 Citations (Scopus)

Abstract

A contrastive distortion-level learning-based no-reference image-quality assessment (NR-IQA) framework is proposed in this study to further effectively model various distortion types with the same or different distortion levels. The proposed method aims to improve the prediction accuracy of NR-IQA. The proposed method consists of three parts: multiscale distortion-level representation learning, single-image NR-IQA, and a representation affinity module, which can reduce NR-IQA computational complexity while maintaining a low-distortion representation of high-distortion inputs. The proposed NR-IQA method aims to extract distributional features of samples in real distorted images and predict ambiguity based on distortion-level learning. Experimental results show that by comparing on many NR-IQA data sets the proposed method can outperform state-of-the-art methods.

Original languageEnglish
Pages (from-to)8199-10040
Number of pages17
JournalInternational Journal of Intelligent Systems
Volume37
Issue number11
Early online date30 Jul 2022
DOIs
Publication statusPublished - Nov 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 Wiley Periodicals LLC.

Keywords

  • contrastive learning
  • no-reference image-quality assessment
  • representation learning
  • unsupervised learning

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