Fast CU Decision-Making Algorithm Based on DenseNet Network for VVC

• Main Authors: Qiuwen Zhang, Ruixiao Guo, Bin Jiang, Rijian Su
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Versatile video coding; convolutional neural network; coding unit partition
• Online Access: https://ieeexplore.ieee.org/document/9523873/

The joint video expert team (JVET) is currently developing a new video coding standard called H.266/Versatile Video Coding (VVC). Compared with High Efficiency Video Coding (HEVC), VVC has added a variety of coding tools. These tools have greatly improved video compression efficiency and maintained a high level video quality. However, due to the increase in computational complexity, the encoding time is much longer than HEVC. We propose a prediction tool based on DenseNet (a convolutional neural network) to decrease the VVC coding complexity. We predict the probability that the edge of <inline-formula> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> blocks in each <inline-formula> <tex-math notation="LaTeX">$64 \times 64$ </tex-math></inline-formula> block is the division boundary by Convolutional Neural Networks (CNN). Then, we skip the unnecessary rate distortion optimization (RDO) and speed up the coding by probability vectors in advance. The proposed method can reduce the coding complexity of 46.10&#x0025; in VTM10.0 intra coding, while Bj&#x00F8;ntegaard delta bit rate (BDBR) only increases by 1.86&#x0025;. In the sequence with a resolution greater than 1080P, the acceleration efficiency can be at 64.81&#x0025;, the BDBR loss only increased by 1.92&#x0025;.