Underwater image quality assessment method via the fusion of visual and structural information

Abstract

Underwater-captured images often suffer from quality degradation due to the challenging underwater environment, leading to information loss that significantly affects their usability. Therefore, accurately predicting the quality of underwater images is crucial. To tackle this issue, this study introduces a novel Underwater Image Quality Assessment method that combines visual and structural information. First, the CIELab map, gradient feature map, and Mean Subtracted Contrast Normalized feature map of the underwater image are obtained. Then, these feature maps are divided into non-overlapping 32x32 patches, and each patch is fed into the corresponding sub-network. This method allows for a comprehensive description of the changes in visual and structural information resulting from quality degradation in underwater images. Subsequently, the features extracted by the multipath network are fused using a feature fusion network to promote feature complementarity and overcome the limitations of individual features. Finally, the relationship between underwater image quality and fusion features was learned to obtain an evaluation model. Furthermore, the quality of the underwater image can be measured by combining the quality prediction scores of different patches. Experimental results on underwater image datasets demonstrate that the proposed method can achieve more accurate and stable quality measurement results with a more lightweight structure. Meanwhile, performance comparisons on natural image datasets and screen content image datasets confirm that the proposed method is more applicable for complex application scenarios than existing methods. The code is open-source and available at https://github.com/dart-into/UIQAVSI.