Enhancing Quality for HEVC Compressed Videos
| Autor: | Tie Liu, Zhenyu Guan, Ren Yang, Mai Xu, Zulin Wang |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer science business.industry 02 engineering and technology Convolutional neural network Multimedia (cs.MM) Video compression picture types Visualization Computer engineering Distortion 0202 electrical engineering electronic engineering information engineering Media Technology 020201 artificial intelligence & image processing The Internet Electrical and Electronic Engineering business Encoder Computer Science - Multimedia Transform coding Decoding methods |
| Zdroj: | IEEE Transactions on Circuits and Systems for Video Technology. 29:2039-2054 |
| ISSN: | 1558-2205 1051-8215 |
| Popis: | 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 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 frames. Our experimental results validate that our QE-CNN method is effective in enhancing quality for both I and P 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. Comment: Submitted to IEEE T-CSVT |
| Databáze: | OpenAIRE |
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