Deep Quality Assessment of Compressed Videos: A Subjective and Objective Study

TL;DR

This paper introduces a large-scale no-reference compressed video quality assessment method using a 3D CNN trained on a new semi-automatically labeled database, outperforming existing metrics.

Contribution

It presents the CVSAR database and a novel STFEE model for no-reference video quality assessment, advancing the accuracy and generalization of quality evaluation.

Findings

STFEE outperforms state-of-the-art metrics.

The CVSAR database is the largest of its kind.

The method shows strong cross-database generalization.

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.