Underwater Image Quality Assessment: Benchmark Database and Objective Method

Underwater image quality assessment (UIQA) plays a crucial role in monitoring and detecting the quality of acquired underwater images in underwater imaging systems. Currently, the investigation of UIQA encounters two major challenges. First, a lack of large-scale UIQA databases for benchmarking UIQA algorithms remains, which greatly restricts the development of UIQA research. The other limitation is that there is a shortage of effective UIQA methods that can faithfully predict underwater image quality. To alleviate these two challenges, in this paper, we first construct a large-scale UIQA database (UIQD). Specifically, UIQD contains a total of 5369 authentic underwater images that span abundant underwater scenes and typical quality degradation conditions. Extensive subjective experiments are executed to annotate the perceived quality of the underwater images in UIQD. Based on an in-depth analysis of underwater image characteristics, we further establish a novel baseline UIQA metric that integrates channel and spatial attention mechanisms and a transformer. Channel- and spatial attention modules are used to capture the image channel and local quality degradations, while the transformer module characterizes the image quality from a global perspective. Multilayer perception is employed to fuse the local and global feature representations and yield the image quality score. Extensive experiments conducted on UIQD demonstrate that the proposed UIQA model achieves superior prediction performance compared with the state-of-the-art UIQA and IQA methods. The proposed UIQD and UIQA models will be released at https://github.com/YT2015?tab=repositories.