Blind image quality assessment via adaptive graph attention

Wang, Huasheng, Liu, Jiang, Tan, Hongchen, Lou, Jianxun, Liu, Xiaochang, Zhou, Wei and Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481 2024. Blind image quality assessment via adaptive graph attention. IEEE Transactions on Circuits and Systems for Video Technology 10.1109/TCSVT.2024.3405789

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Abstract

Recent advancements in blind image quality assessment (BIQA) are primarily propelled by deep learning technologies. While leveraging transformers can effectively capture long-range dependencies and contextual details in images, the significance of local information in image quality assessment can be undervalued. To address this challenging problem, we propose a novel feature enhancement framework tailored for BIQA. Specifically, we devise an Adaptive Graph Attention (AGA) module to simultaneously augment both local and contextual information. It not only refines the post-transformer features into an adaptive graph, facilitating local information enhancement, but also exploits interactions amongst diverse feature channels. The proposed technique can better reduce redundant information introduced during feature updates compared to traditional convolution layers, streamlining the self-updating process for feature maps. Experimental results show that our proposed model outperforms state-of-the-art BIQA models in predicting the perceived quality of images. The code of the model will be made publicly available.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Computer Science & Informatics
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1051-8215
Date of First Compliant Deposit:	8 June 2024
Date of Acceptance:	22 May 2024
Last Modified:	25 Jun 2024 15:01
URI:	https://orca.cardiff.ac.uk/id/eprint/169619

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