Sang Quang Nguyen, Mr., Tien Huu Vu, Dr., Duong Dinh Trieu, Dr., Minh Dinh Bao, Mr., Minh Do Ngoc, Mr., Xiem Hoang Van

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Versatile Video Coding (VVC) has been recently becoming popular in coding video data due to its compression efficiency. To reach this performance, Joint Video Experts Team (JVET) has introduced a number of coding improvement techniques to VVC model. Among them, VVC Intra coding proposed a new concept of quad-tree nested multi-type tree (QTMT) and extended the predicted modes with up to 67 options. As a result, the complexity of the VVC Intra encoding also greatly increases. To make VVC Intra coding more feasible in real time applications, we propose in this paper a novel fast mode decision method together with a deep learning based fast QTMT. At the first stage, we use a learned convolutional neural network (CNN) to predict the coding unit map and then fed into the VVC encoder to early terminate the block partitioning process. After that, we design a statistical model to predict a list of most probable modes (MPM) for each selected Coding Unit (CU) size. Finally, we introduce a novel three-steps mode decision algorithm to estimate the optimal directional mode without sacrificing the compression performance. The proposed early CU splitting and fast intra prediction are integrated into the latest VTM reference software. Experimental results show that the proposed method can save 50.2% encoding time with a negligible BD-Rate increase.

Keywords: VVC Intra coding, Early-Terminate Hierarchical, CNN, Most probable mode (MPM).