Capturing Co-existing Distortions in User-Generated Content for No-reference Video Quality Assessment

• URL: http://arxiv.org/abs/2307.16813v1
• Date: Mon, 31 Jul 2023 16:29:29 GMT
• Title: Capturing Co-existing Distortions in User-Generated Content for No-reference Video Quality Assessment
• Authors: Kun Yuan, Zishang Kong, Chuanchuan Zheng, Ming Sun, Xing Wen
• Abstract summary: Video Quality Assessment (VQA) aims to predict the perceptual quality of a video. VQA faces two under-estimated challenges unresolved in User Generated Content (UGC) videos. We propose textitVisual Quality Transformer (VQT) to extract quality-related sparse features more efficiently.
• Score: 9.883856205077022
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video Quality Assessment (VQA), which aims to predict the perceptual quality of a video, has attracted raising attention with the rapid development of streaming media technology, such as Facebook, TikTok, Kwai, and so on. Compared with other sequence-based visual tasks (\textit{e.g.,} action recognition), VQA faces two under-estimated challenges unresolved in User Generated Content (UGC) videos. \textit{First}, it is not rare that several frames containing serious distortions (\textit{e.g.,}blocking, blurriness), can determine the perceptual quality of the whole video, while other sequence-based tasks require more frames of equal importance for representations. \textit{Second}, the perceptual quality of a video exhibits a multi-distortion distribution, due to the differences in the duration and probability of occurrence for various distortions. In order to solve the above challenges, we propose \textit{Visual Quality Transformer (VQT)} to extract quality-related sparse features more efficiently. Methodologically, a Sparse Temporal Attention (STA) is proposed to sample keyframes by analyzing the temporal correlation between frames, which reduces the computational complexity from $O(T^2)$ to $O(T \log T)$. Structurally, a Multi-Pathway Temporal Network (MPTN) utilizes multiple STA modules with different degrees of sparsity in parallel, capturing co-existing distortions in a video. Experimentally, VQT demonstrates superior performance than many \textit{state-of-the-art} methods in three public no-reference VQA datasets. Furthermore, VQT shows better performance in four full-reference VQA datasets against widely-adopted industrial algorithms (\textit{i.e.,} VMAF and AVQT).

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