Underwater Image Quality Assessment from Synthetic to Real-world: Dataset and Objective Method

Abstract

The complicated underwater environment and lighting conditions lead to severe influence on the quality of underwater imaging, which tends to impair underwater exploration and research. To effectively evaluate the quality of underwater images, an underwater image quality assessment dataset is constructed from synthetic to real-world, and then a new objective underwater image assessment method based on the characteristics of the underwater imaging is proposed (UICQA). Specifically, to address the lack of a publicly available datasets and more accurately quantify the quality of underwater images, a subjective underwater image quality assessment dataset from synthetic to real-world underwater images, named USRD, is constructed. Considering that the transmission map can effectively reflect the characteristics of the underwater imaging, statistical features are effectively extracted from the transmission map for distinguishing underwater images of different quality. Further, considering that the transmission map negatively correlates with scene depth, a local-to-global transmission map weighted contrast feature is constructed. Additionally, the color features of human perception and texture features based on fractal dimensions are proposed. Finally, the experimental results show that the proposed UICQA method exhibits the highest correlation with ground truth scores compared to state-of-the-art UIQA methods.