Learning-Based Fast Depth Inter Coding for 3D-HEVC via XGBoost

Zixiang Zhang, Li Yu, Jian Qian, Hongkui Wang

Abstract: The 3D extension of High Efficiency Video Coding (3D-HEVC) achieves excellent per-formance for 3D video coding while possessing significant computational complexity. To accelerate the time-consuming coding process of the depth map, a fast algorithm via XG-Boost is proposed in this paper. Specifically, a total of 14 specialized XGBoost models are used for different block sizes and viewpoint types to achieve early coding unit partition de-termination (ECP) and early prediction unit mode selection (EPM) to avoid executing the exhaustive traversal coding process. To promote the prediction accuracy of XGBoost mod-els, multi-domain correlations, including spatiotemporal, inter-view, and inter-component correlations are utilized and plenty of features are selected for model training. Evaluated on HTM-16.0 under random access configuration, the proposed ECP strategy can obtain 51.2% total encoding time saving with a 0.18% BDBR increase and the ECP+EPM can overall achieve 60.8% total encoding time saving with a 0.59% BDBR increase. The source code of our method is available at https://github.com/Joeyrr/_EPM.git.