| Summary: | Underwater videos often suffer from quality degradation. On the one hand, the exponential attenuation of the natural light in water media leads to the loss of underwater video quality. On the other hand, the video is unstable due to the underwater shooting environment such as flow and pressure. Considering the influence of underwater video spatial-temporal features and motion features on video quality, this paper proposes an objective no-reference video quality assessment (VQA) method for underwater scenes, named TS-UVQA (two-stream underwater video quality assessment). TS-UVQA adopts two-stream network structure: Spatial-temporal Net including three-dimensional convolution, switchable normalization and slow fusion strategy is designed to extract spatial-temporal features from the original video frames; Motion Net including two-dimensional convolution and switchable norm-alization is designed to extract motion features from the optical flow fields. Then, decision-level linear fusion is used to aggregate video features and realize high-precision underwater VQA. This paper takes the Pearson linear correlation coefficient (PLCC) and Spearman order correlation coefficient (SROCC) of the video quality assessment result and subjective quality score as indicators. Experimental results show that the motion characteristics extracted from the optical flow can enhance the performance of underwater VQA. Compared with 13 image/video quality assessment methods in an underwater video dataset, TS-UVQA achieves the best performance in terms of PLCC and SROCC. Further experiments on the natural scene video datasets, ECVQ, EVVQ and LIVE achieve a close perfor-mance to the state-of-the-art VQA method, and show that TS-UVQA has good generalization to nature scene VQA.
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