Underwater Image Quality Assessment: A Perceptual Framework Guided by Physical Imaging

• URL: http://arxiv.org/abs/2412.15527v1
• Date: Fri, 20 Dec 2024 03:31:45 GMT
• Title: Underwater Image Quality Assessment: A Perceptual Framework Guided by Physical Imaging
• Authors: Weizhi Xian, Mingliang Zhou, Leong Hou U, Lang Shujun, Bin Fang, Tao Xiang, Zhaowei Shang,
• Abstract summary: We propose a physically imaging-guided framework for underwater image quality assessment (UIQA) called PIGUIQA. We incorporate advanced physics-based underwater imaging estimation into our method and define distortion metrics that measure the impact of direct transmission attenuation and backwards scattering on image quality. PIGUIQA achieves state-of-the-art performance in underwater image quality prediction and exhibits strong generalizability.
• Score: 52.860312888450096
• License:
• Abstract: In this paper, we propose a physically imaging-guided framework for underwater image quality assessment (UIQA), called PIGUIQA. First, we formulate UIQA as a comprehensive problem that considers the combined effects of direct transmission attenuation and backwards scattering on image perception. On this basis, we incorporate advanced physics-based underwater imaging estimation into our method and define distortion metrics that measure the impact of direct transmission attenuation and backwards scattering on image quality. Second, acknowledging the significant content differences across various regions of an image and the varying perceptual sensitivity to distortions in these regions, we design a local perceptual module on the basis of the neighborhood attention mechanism. This module effectively captures subtle features in images, thereby enhancing the adaptive perception of distortions on the basis of local information. Finally, by employing a global perceptual module to further integrate the original image content with underwater image distortion information, the proposed model can accurately predict the image quality score. Comprehensive experiments demonstrate that PIGUIQA achieves state-of-the-art performance in underwater image quality prediction and exhibits strong generalizability. The code for PIGUIQA is available on https://anonymous.4open.science/r/PIGUIQA-A465/

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