Abstract
Video quality assessment is critical in optimizing video coding techniques. However, the state-of-the-art methods have limited performance, which is largely due to the lack of large-scale subjective databases for training. In this work, a semi-automatic labeling method is adopted to build a large-scale compressed video quality database, which allows us to label a large number of compressed videos with manageable human workload. The resulting Compressed Video quality database with Semi-Automatic Ratings (CVSAR), so far the largest of compressed video quality database. We train a no-reference compressed video quality assessment model with a 3D CNN for SpatioTemporal Feature Extraction and Evaluation (STFEE). Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics and achieves promising generalization performance in cross-database tests. The CVSAR database has been made publicly available. It can be accessed at https://github.com/Rocknroll194/CVSAR.
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