Enhancing Quality for HEVC Compressed Videos

By Ren Yang, Mai Xu, Tie Liu, Zulin Wang, Zhenyu Guan. 2017

211 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Mai Xu
2. Zulin Wang
3. Ren Yang
4. Tianyi Li
5. Chongyang Zhang
6. Xiaoyun Zhang
7. Qiang Hu
8. Xintong Han

In Signal Processing

1. Mai Xu
2. Zulin Wang
3. Ren Yang
4. Tianyi Li
5. Xintong Han

Citations in the corpus

• Enhancing HEVC Compressed Videos with a Partition-Masked Convolutional Neural Network (2018, methodology): “Five methods are compared in Table 2: (1) VRCNN [4] which is a benchmark CNN-based compressed-video enhancement method; (2) QECNN-P [5] which is a state-ofthe-art compressed-video enhancement method for P frames in HEVC; (3) Our (1-in), which is the single input baseline of our approach; (4) VRCNN+M”
• Multi-frame Quality Enhancement for Compressed Video (2018, methodology): “designed a Decoder-side Scalable CNN (DS-CNN) for video quality enhancement [38, 39].”