PIGUIQA: A Physical Imaging Guided Perceptual Framework for Underwater Image Quality Assessment

TL;DR

PIGUIQA introduces a physics-based, perceptual framework for underwater image quality assessment that combines radiative transfer theory, local attention, and global perception to achieve state-of-the-art results.

Contribution

It integrates physics-based imaging estimations with perceptual modeling, addressing spatial and perceptual variations for improved underwater image quality assessment.

Findings

Achieves state-of-the-art performance on multiple benchmarks.

Demonstrates robust cross-dataset generalization.

Effectively captures subtle local distortions and holistic scene information.

Abstract

In this paper, we propose a Physical Imaging Guided perceptual framework for Underwater Image Quality Assessment (UIQA), termed PIGUIQA. First, we formulate UIQA as a comprehensive problem that considers the combined effects of direct transmission attenuation and backward scattering on image perception. By leveraging underwater radiative transfer theory, we systematically integrate physics-based imaging estimations to establish quantitative metrics for these distortions. Second, recognizing spatial variations in image content significance and human perceptual sensitivity to distortions, we design a module built upon a neighborhood attention mechanism for local perception of images. This module effectively captures subtle features in images, thereby enhancing the adaptive perception of distortions on the basis of local information. Third, by employing a global perceptual aggregator that further integrates holistic image scene with underwater distortion information, the proposed model accurately predicts image quality scores. Extensive experiments across multiple benchmarks demonstrate that PIGUIQA achieves state-of-the-art performance while maintaining robust cross-dataset generalizability. The implementation is publicly available at https://github.com/WeizhiXian/PIGUIQA