Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment

• URL: http://arxiv.org/abs/2304.06440v1
• Date: Thu, 13 Apr 2023 12:18:15 GMT
• Title: Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment
• Authors: Kai Zhao, Kun Yuan, Ming Sun and Xing Wen
• Abstract summary: Video assessment (VQA) aims to simulate the human perception of video quality. We decompose video into three levels: patch level, frame level, and clip level. We propose Zoom-VQA architecture to perceive features at different levels.
• Score: 14.728530703277283
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video quality assessment (VQA) aims to simulate the human perception of video quality, which is influenced by factors ranging from low-level color and texture details to high-level semantic content. To effectively model these complicated quality-related factors, in this paper, we decompose video into three levels (\ie, patch level, frame level, and clip level), and propose a novel Zoom-VQA architecture to perceive spatio-temporal features at different levels. It integrates three components: patch attention module, frame pyramid alignment, and clip ensemble strategy, respectively for capturing region-of-interest in the spatial dimension, multi-level information at different feature levels, and distortions distributed over the temporal dimension. Owing to the comprehensive design, Zoom-VQA obtains state-of-the-art results on four VQA benchmarks and achieves 2nd place in the NTIRE 2023 VQA challenge. Notably, Zoom-VQA has outperformed the previous best results on two subsets of LSVQ, achieving 0.8860 (+1.0%) and 0.7985 (+1.9%) of SRCC on the respective subsets. Adequate ablation studies further verify the effectiveness of each component. Codes and models are released in https://github.com/k-zha14/Zoom-VQA.

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