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.

Bi-prediction is a fundamental module of inter prediction in the blocked-based hybrid video coding framework. Block-based motion estimation(ME) and motion compensation(MC) with simple models are adopted in bi-prediction process. Unfortunately, this MEMC-based scheme can't guarantee the prediction performance when it comes to video with complicated motions. In this paper, a novel inter prediction scheme based on deep frame prediction network (DFP-net) is proposed to enhance bi-prediction accuracy especially in complicate scenes. Specifically, the proposed DFP-net is composed of multi-scale motion alignment, fusion of temporal and spatial correlation and frame synthesis module. The DFP-net can precisely extract and fuse motion features in various scales and completely exploit temporal and spatial correlation to generate the prediction frame in a data-driven manner. Moreover, the DFP-net is integrated into VTM-6.2 to provide an additional prediction frame for biprediction. Since the prediction generated by DFP-net is more similar with to-be-coded frame in the sense of temporal distance and texture, it can be added to reference list to improve the diversity of references. In this manner, the proposed bi-prediction scheme has surpassed VTM-6.2 on average 1.8% BD-rate saving.

Convolutional neural network has witnessed remarkable progress in compressed video quality enhancement in high efficiency video coding (HEVC) standard. But most existing methods focus on single frame quality enhancement where copious temporal and spatial information is neglected. In this paper, we propose a spatial-temporal fusion convolutional neural network (STEF-CNN) to employ spatial and temporal information to improve the performance of in-loop filter in HEVC. Specifically, the STEF-CNN adopts a pre-denoising network which in advance processes the compressed videos frame by frame. The pre-denoising operation alleviates the impact of noise and blocking artifacts. Then the denoised frames are sent to temporal-spatial fusion module which picks out valuable temporal and spatial information. The fused frames are eventually fed to quality enhancement network which is based on residual learning and dense network. The STEF-CNN is capable of capturing abundant information from consecutive neighboring frames. Extensive experimental results demonstrate the effectiveness of the proposed method. The STEF-CNN achieves 11.53% BD-BR reduction in all-intra (AI) configuration and 10.20% BD-BR reduction in random-access (RA) configuration.