Abstract: High Efficiency Video Coding (HEVC) significantly improves the coding efficiency but increases the coding complexity, especially in the process of coding unit (CU) partitioning based on quadtree structure, so it is important to study the fast CU partitioning. A multi-scale feature fusion network can achieve fast HEVC CU partitioning. Therefore, the UcuNet network structure is designed by combining the U-Net and CU partitioning features. Meanwhile, asymmetric convolutional AC and CBAM attention mechanisms are used to enhance the feature extraction of pixels at different scales. In order to sufficiently  train the deep learning model, the original video with different resolutions and the corresponding encoding information are collected to build a large-scale dataset. Finally, the model is embedded into the HEVC coding architecture to predict the result of CU partitioning in advance, which can effectively reduce the coding complexity caused by CU partitioning by eliminating the recursive rate distortion optimization (RDO) calculation process in the original CU partitioning method. Compared with the official HEVC test model (HM16.20), the proposed UcuNet reduces the average coding time by 68.13% while BD-BR is only decreased by 2.63%.

Key words: high efficiency video coding (HEVC), coding unit partitioning, deep learning, asymmetric convolution