No-reference image quality assessment based on a multitask image restoration network

Fan CHEN, Hong FU, Hengyong YU, Ying CHU

Research output: Contribution to journalArticlespeer-review

Abstract

When image quality is evaluated, the human visual system (HVS) infers the details in the image through its internal generative mechanism. In this process, the HVS integrates both local and global information about the image, utilizes contextual information to restore the original image information, and compares it with the distorted image information for image quality evaluation. Inspired by this mechanism, a no-reference image quality assessment method is proposed based on a multitask image restoration network. The multitask image restoration network generates a pseudo-reference image as the main task and produces a structural similarity index measure map as an auxiliary task. By mutually promoting the two tasks, a higher-quality pseudo-reference image is generated. In addition, when predicting the image quality score, both the quality restoration features and the difference features between the distorted and reference images are used, thereby fully utilizing the information from the pseudo-reference image. In order to facilitate the model’s ability to extract both global and local features, we introduce a multi-scale feature fusion module. Experimental results demonstrate that the proposed method achieves excellent performance on both synthetically and authentically distorted databases. Copyright © 2023 by the authors.

Original languageEnglish
Article number6802
JournalApplied Sciences (Switzerland)
Volume13
Issue number11
DOIs
Publication statusPublished - Jun 2023

Citation

Chen, F., Fu, H., Yu, H., & Chu, Y. (2023). No-reference image quality assessment based on a multitask image restoration network. Applied Sciences, 13(11), Article 6802. https://doi.org/10.3390/app13116802

Keywords

  • No-reference image quality assessment
  • Multitask learning
  • Image restoration
  • Multi-level features

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