Chang, Yongli, Yue, Guanghui, Zhao, Bo, Yu, Li, Ju, Yakun, Amirpour, Hadi, Gabbouj, Moncef and Zhou, Wei
2026.
Perception-inspired network for stereo image quality assessment.
IEEE Transactions on Image Processing
10.1109/tip.2026.3680564
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Abstract
Existing stereo image quality assessment (SIQA) methods generally have limitations in binocular fusion and fine-grained perception modeling. To address these issues, we propose a Perception-Inspired Network for SIQA that simulates binocular difference-guided fusion, high-frequency sensitivity, and hierarchical perception mechanisms of the human visual system (HVS). First, a difference-guided binocular fusion (DGBF) module is designed to mimic the binocular difference sensitivity mechanism, which exploits difference information at both the feature-level and image-level to optimize binocular fusion. Furthermore, the image distortion primarily affects the high-frequency components, which are critical for perceptual quality. To reflect this, we propose a high-frequency enhancement module (HFEM) to simulate the human eye’s sensitivity to edge and texture distortions. Finally, to better achieve fine-grained perception modeling, we propose a hierarchical quality regression strategy that simulates the human perceptual process, from perceiving local details to forming a global quality judgment, thereby achieving a quality prediction more aligned with human subjective evaluation. Experimental results demonstrate that the proposed method outperforms mainstream approaches, achieving a PLCC of 0.9734 on the LIVE I database, and a PLCC of 0.9632 on the LIVE II database.
| Item Type: | Article |
|---|---|
| Date Type: | Published Online |
| Status: | Published |
| 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: 2026-01-01 |
| Publisher: | Institute of Electrical and Electronics Engineers |
| ISSN: | 1057-7149 |
| Date of First Compliant Deposit: | 24 April 2026 |
| Last Modified: | 24 Apr 2026 10:00 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/186605 |
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