Paper Info
Title
Enhancing Quality For Hevc Compressed Videos
Abstract
The latest High Efficiency Video Coding (HEVC) standard has been increasingly applied to generate video streams over the Internet. However, HEVC compressed videos may incur severe quality degradation, particularly at low bit rates. Thus, it is necessary to enhance the visual quality of HEVC videos at the decoder side. To this end, this paper proposes a quality enhancement convolutional neural network (QE-CNN) method that does not require any modification of the encoder to achieve quality enhancement for HEVC. In particular, our QE-CNN method learns QE-CNN-I and QE-CNN-P models to reduce the distortion of HEVC I and P/B frames, respectively. The proposed method differs from the existing CNN-based quality enhancement approaches, which only handle intra-coding distortion and are thus not suitable for P/B frames. Our experimental results validate that our QE-CNN method is effective in enhancing quality for both I and P/B frames of HEVC videos. To apply our QE-CNN method in time-constrained scenarios, we further propose a time-constrained quality enhancement optimization (TQEO) scheme. Our TQEO scheme controls the computational time of QE-CNN to meet a target, meanwhile maximizing the quality enhancement. Next, the experimental results demonstrate the effectiveness of our TQEO scheme from the aspects of time control accuracy and quality enhancement under different time constraints. Finally, we design a prototype to implement our TQEO scheme in a real-time scenario.
Year
DOI
Venue
2019
10.1109/TCSVT.2018.2867568
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Keywords
DocType
Volume
HEVC, quality enhancement, convolutional neural network
Journal
29
Issue
ISSN
Citations 
7
1051-8215
6
PageRank 
References 
Authors
0.45
0
5
Name
Order
Citations
PageRank
Ren Yang1648.19
Mai Xu250957.90
Tie Liu3212.00
Zulin Wang421629.63
Zhenyu Guan511515.83