Decoder-side HEVC quality enhancement with scalable convolutional neural network

Abstract

The latest High Efficiency Video Coding (HEVC) has been increasingly used to generate video streams over Internet. However, the decoded HEVC video streams may incur severe quality degradation, especially at low bit-rates. Thus, it is necessary to enhance visual quality of HEVC videos at the decoder side. To this end, we propose in this paper a Decoder-side Scalable Convolutional Neural Network (DS-CNN) approach to achieve quality enhancement for HEVC, which does not require any modification of the encoder. In particular, our DS-CNN approach learns a model of Convo-lutional Neural Network (CNN) to reduce distortion of both I and B/P frames in HEVC. It is different from the existing CNN-based quality enhancement approaches, which only handle intra coding distortion, thus not suitable for B/P frames. Furthermore, a scalable structure is included in our DS-CNN, suchthat the computational complexity of our DS-CNN approach is adjustable to the changing computational resources. Finally, the experimental results show the effectiveness of our DS-CNN approach in enhancing quality for both I and B/P frames of HEVC.