Deep Quality Assessment of Compressed Videos: A Subjective and Objective Study
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by
Liqun Lin, Zheng Wang, Jiachen He, Weiling Chen, Yiwen Xu, Tiesong Zhao
2022
Abstract
In the video coding process, the perceived quality of a compressed video is
evaluated by full-reference quality evaluation metrics. However, it is
difficult to obtain reference videos with perfect quality. To solve this
problem, it is critical to design no-reference compressed video quality
assessment algorithms, which assists in measuring the quality of experience on
the server side and resource allocation on the network side. Convolutional
Neural Network (CNN) has shown its advantage in Video Quality Assessment (VQA)
with promising successes in recent years. A large-scale quality database is
very important for learning accurate and powerful compressed video quality
metrics. 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 and STFEE model will be made publicly available to facilitate
reproducible research.
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