A Deep Learning based No-reference Quality Assessment Model for UGC Videos

• URL: http://arxiv.org/abs/2204.14047v1
• Date: Fri, 29 Apr 2022 12:45:21 GMT
• Title: A Deep Learning based No-reference Quality Assessment Model for UGC Videos
• Authors: Wei Sun, Xiongkuo Min, Wei Lu, Guangtao Zhai
• Abstract summary: Previous video quality assessment (VQA) studies either use the image recognition model or the image quality assessment (IQA) models to extract frame-level features of videos for quality regression. We propose a very simple but effective VQA model, which trains an end-to-end spatial feature extraction network to learn the quality-aware spatial feature representation from raw pixels of the video frames. With the better quality-aware features, we only use the simple multilayer perception layer (MLP) network to regress them into the chunk-level quality scores, and then the temporal average pooling strategy is adopted to obtain the video
• Score: 44.00578772367465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quality assessment for User Generated Content (UGC) videos plays an important role in ensuring the viewing experience of end-users. Previous UGC video quality assessment (VQA) studies either use the image recognition model or the image quality assessment (IQA) models to extract frame-level features of UGC videos for quality regression, which are regarded as the sub-optimal solutions because of the domain shifts between these tasks and the UGC VQA task. In this paper, we propose a very simple but effective UGC VQA model, which tries to address this problem by training an end-to-end spatial feature extraction network to directly learn the quality-aware spatial feature representation from raw pixels of the video frames. We also extract the motion features to measure the temporal-related distortions that the spatial features cannot model. The proposed model utilizes very sparse frames to extract spatial features and dense frames (i.e. the video chunk) with a very low spatial resolution to extract motion features, which thereby has low computational complexity. With the better quality-aware features, we only use the simple multilayer perception layer (MLP) network to regress them into the chunk-level quality scores, and then the temporal average pooling strategy is adopted to obtain the video-level quality score. We further introduce a multi-scale quality fusion strategy to solve the problem of VQA across different spatial resolutions, where the multi-scale weights are obtained from the contrast sensitivity function of the human visual system. The experimental results show that the proposed model achieves the best performance on five popular UGC VQA databases, which demonstrates the effectiveness of the proposed model. The code will be publicly available.

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