Optimizing intra coding in 3D-HEVC with DMP: a deep learning-based depth modeling mode prediction strategy

3D-HEVC is the latest 3D video coding standard which encodes a small number of texture and depth maps of a few viewpoints by Multi-view plus depth (MVD) format. The depth map is essential to synthesize virtual viewpoints for display. To ensure the quality of depth map coding, depth modeling modes (DMMs) are introduced and added to the candidate list directly for RD Cost calculation of all PUs to protect the edge information of depth map. Thus, increase the computational burden significantly. A depth mode prediction CNN, named DMP-CNN, is proposed in this paper to decide whether DMM modes should be added to the candidate list or not, and skip unnecessary mode decision calculations. Experimental results show that the proposed method achieves 14.64% of coding time saving compared to HTM-16.0 with 1.41% PSNR degradation of synthesis viewpoints at the same bitrate.