Attention integrated hierarchical networks for no-reference image quality assessment

Junyong You*, Jari Korhonen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)
2 Downloads (Pure)


Quality assessment of natural images is influenced by perceptual mechanisms, e.g., attention and contrast sensitivity, and quality perception can be generated in a hierarchical process. This paper proposes an architecture of Attention Integrated Hierarchical Image Quality networks (AIHIQnet) for no-reference quality assessment. AIHIQnet consists of three components: general backbone network, perceptually guided neck network, and head network. Multi-scale features extracted from the backbone network are fused to simulate image quality perception in a hierarchical manner. The attention and contrast sensitivity mechanisms modelled by an attention module capture essential information for quality perception. Considering that image rescaling potentially affects perceived quality, appropriate pooling methods in the non-convolution layers in AIHIQnet are employed to accept images with arbitrary resolutions. Comprehensive experiments on publicly available databases demonstrate outstanding performance of AIHIQnet compared to state-of-the-art models. Ablation experiments were performed to investigate the variants of the proposed architecture and reveal importance of individual components.

Original languageEnglish
Article number103399
JournalJournal of Visual Communication and Image Representation
Early online date13 Dec 2021
Publication statusPublished - Jan 2022

Bibliographical note

Funding Information:
This work is in part supported by the basic grant (Grunnbevilgning) of NORCE funded by the Research Council of Norway, and in part by National Natural Science Foundation of China under Grant 61772348 , Guangdong ” Pearl River Talent Recruitment Program ” under Grant 2019ZT08X603 , and Shenzhen Fundamental Research Program under Grant JCYJ20200109110410133.

Publisher Copyright:
© 2021 NORCE Norwegian Research Centre AS


  • Attention
  • Hierarchical networks
  • Image quality assessment (IQA)
  • Perceptual mechanisms
  • Quality perception


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