Perception-oriented bidirectional attention network for image super-resolution quality assessment

Li, Yixiao, Yang, Xiaoyuan, Yue, Guanghui, Fu, Jun, Jiang, Qiuping, Jia, Xu, Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884, Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481 and Zhou, Wei 2025. Perception-oriented bidirectional attention network for image super-resolution quality assessment. IEEE Transactions on Image Processing 10.1109/tip.2025.3633145

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Abstract

Many super-resolution (SR) algorithms have been proposed to increase image resolution. However, full-reference (FR) image quality assessment (IQA) metrics for comparing and evaluating different SR algorithms are limited. In this work, we propose the Perception-oriented Bidirectional Attention Network (PBAN) for image SR FR-IQA, which is composed of three modules: an image encoder module, a perception-oriented bidirectional attention (PBA) module, and a quality prediction module. First, we encode the input images for feature representations. Inspired by the characteristics of the human visual system, we then construct the perception-oriented PBA module. Specifically, different from existing attention-based SR IQA methods, we conceive a Bidirectional Attention to bidirectionally construct visual attention to distortion, which is consistent with the generation and evaluation processes of SR images. To further guide the quality assessment towards the perception of distorted information, we propose Grouped Multi-scale Deformable Convolution, enabling the proposed method to adaptively perceive distortion. Moreover, we design Sub-information Excitation Convolution to direct visual perception to both sub-pixel and sub-channel attention. Finally, the quality prediction module is exploited to integrate quality-aware features and regress quality scores. Extensive experiments demonstrate that our proposed PBAN outperforms state-of-the-art quality assessment methods.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html, Start Date: 2025-01-01
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1057-7149
Last Modified:	01 Dec 2025 13:30
URI:	https://orca.cardiff.ac.uk/id/eprint/182772

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