Fast Prediction of Ternary Tree Partition for Efficient VVC Intra Coding

In versatile video coding (VVC) intra coding, the partition pattern depends on the rate-distortion optimization process, which is time-consuming and has a great impact on the overall coding efficiency. Hence, in this paper, a fast decision mechanism is proposed for ternary tree partition based on the LightGBM model aiming to improve the decision-making efficiency by skipping the calculation process of rate-distortion cost. Firstly, five features of each coding unit (CU) are selected based on their importance to the optimal partition pattern. Secondly, the selected five features are employed to train the LightGBM models and optimize the parameters. Finally, the trained models are embedded into the VTM 4.0 platform to predict whether to use or skip the ternary tree partition pattern for each CU. Theoretically, the proposed mechanism can effectively reduce the VVC intra coding complexity. Experiments are conducted and the results show that the proposed scheme can save 46.46% encoding time with only 0.56% BDBR increase and 0.03% BD-PSNR decrease compared with VTM4.0, out forming most of the existing major methods.