As the amounts of images and videos are now growing explosively, there is a constant hunger for higher quality video compression algorithms. Inspired by the great success of neural network in many disciplines, in this thesis, I focus on improving the efficiency of video coding based on neural network. First, a quality enhancement network for versatile video coding (VVC) is proposed, which consists of a temporal fusion subnet and a spatial detail enhancement subnet to jointly explore effiective prior information. Second, I present a robust multi-frame guided attention network (MGANet) for HEVC compressed videos. The combination of an advanced motion flow algorithm and a temporal encoder greatly improves its ability to explore temporal information. In addition, the partition information o...[ Read more ]

As the amounts of images and videos are now growing explosively, there is a constant hunger for higher quality video compression algorithms. Inspired by the great success of neural network in many disciplines, in this thesis, I focus on improving the efficiency of video coding based on neural network. First, a quality enhancement network for versatile video coding (VVC) is proposed, which consists of a temporal fusion subnet and a spatial detail enhancement subnet to jointly explore effiective prior information. Second, I present a robust multi-frame guided attention network (MGANet) for HEVC compressed videos. The combination of an advanced motion flow algorithm and a temporal encoder greatly improves its ability to explore temporal information. In addition, the partition information of transform unit is employed to guide the network to focus on the coding block boundary. Third, a guided attention generative network (GAGNet) is first proposed to generate high-quality frames. Then, based on GAGNet, an adversarial network is designed to generate higher quality reconstructed videos, and the generator is trained by adding a generative loss term to recover more high-frequency information of compressed videos. Fourth, I develop an end-to-end learned image compression framework with large capacity and low redundancy of latent representation (LICLL). Two novel enhancement modules are designed to enhance the R-D performance of the proposed network. Simulation results demonstrate that these proposed methods achieve excellent performance. In particular, compared with the state-of-the-art methods, MGANet achieves 0.6002 dB and 1.0934 dB gains under AI and LDP configurations, respectively. In addition, LICLL achieves more than 1 dB coding gain on the widely used high-resolution test image set.