Enhancing Quality for HEVC Compressed Videos
This work addresses video quality enhancement for HEVC decoders, offering a practical solution for real-time applications, though it is incremental as it builds on existing CNN-based approaches by extending them to handle both frame types.
The paper tackles the problem of severe quality degradation in HEVC compressed videos at low bit-rates by proposing a Quality Enhancement Convolutional Neural Network (QE-CNN) method that enhances both I and P frames without encoder modifications, and introduces a Time-constrained Quality Enhancement Optimization (TQEO) scheme to control computational time while maximizing quality, with experimental results validating its effectiveness in real-time scenarios.
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.