Fast Prediction Algorithm in High Efficiency Video Coding Intra-mode Based on Deep Feature Learning

Compared to H.264/AVC coding standard, High Efficiency Video Coding (HEVC) improves the compression efficiency, but the consequent disadvantage is the significant increase in encoding complexity by using the quad-tree partition. A Multi-Layer Feature Transfer Convolutional Neural Network (MLFT-CNN) for Coding Unit (CU) division and characterization vector prediction in HEVC intra coding mode is proposed, which greatly reduces the complexity of video coding. Firstly, a reduced-resolution feature extraction module incorporating CU partition structure information is proposed. Then, the channel attention mechanism is improved for a better texture expression performance of the feature. After that, the feature transfer mechanism is designed to use the feature division of high-depth coding unit to guide the division of low-depth coding unit. Finally, the target loss function represented by the segmented feature is established, and the end-to-end CU division represents the vector prediction network. The experimental results show that the proposed algorithm effectively reduces the encoding complexity of HEVC without affecting the video coding quality. Specifically, compared to the standard method, the encoding complexity on the standard test sequence is reduced by 70.96% on average.