Perception-Aware Underwater Image Quality Assessment: Dataset, Perceptual Quality Scores, and Assessment Network

Underwater Image Quality Assessment (UIQA) plays an important role in assess the effectiveness of Underwater Image Enhancement (UIE) algorithms or to evaluate the quality of underwater images. However, accurate UIQA that are consistent with human perception remains challenging. This dilemma on one hand is attributed to the lack of real human visual perception UIQA data, and on the other hand that the quality feature representation used by existing UIQA algorithms are inconsistent with human perceptions. To address these issues, we introduce a Large scale Underwater Image Quality Dataset (LUIQD), and propose an UIQA network named as Perception-Aware Underwater image Quality Assessment Network (PAUQA-Net). Specifically, the LUIQD includes 64,180 real and enhance underwater images covering a wide range of scenes, target and imaging conditions, with their perceptual quality scores. Based on the analysis of the mechanisms of human perception, we further design the data-driven PAUQA-Net that integrates an efficient convolutional attention vision Transformer to extract multi-scale features by a multi-path structure. Considering the specificity of human perception of underwater images, color and sharpness features from the chrominance and luminance domains are extracted and fused with local and global images features for joint feature interaction. Extensive experiments conduted on LUIQD and other datasets demonstrate that the proposed PAUQA-Net achieves superior assessment performance compared with the most popular UIQA and IQA methods. The code and dataset can be found in https://github.com/CatchACat083/PAUQA.