Sang Quang Nguyen, Mr., Tien Huu Vu, Dr., Duong Trieu Dinh, Dr., Minh Bao Dinh, Mr., Minh Ngoc Do, Mr., xiem hoang

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Versatile Video Coding (VVC) has been recently becoming popular in coding videos due to its compression efficiency. To reach this performance, Joint Video Experts Team (JVET) has introduced a number of improvement techniques to VVC coding model. Among them, VVC Intra coding introduces a new concept of quad-tree nested multi-type tree (QTMT) and extends 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 deep learning based fast QTMT and an early mode prediction method. At the first stage, we use a learned convolutional neural network (CNN) model 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 using (CU) size. Finally, we employ a so-called 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% of encoding time with a negligible BD-Rate increase.