Abstract
High-Efficiency Video Coding (HEVC)-based 3D video coding (3D-HEVC) is the most recent standard and last exertion of ISO/IEC MPEG and ITU-T Video Coding Experts Group (VCEG) for 3D video coding using a new data video format called Multi-View Video plus Depth map (MVD). This new standard achieves a high coding improvement. In any case, one of the most critical difficulties in 3D-HEVC is time computational complexity. The depth map intra-prediction is a critical factor in 3D-HEVC intra-coding, in which, the 3D-HEVC uses a highly adaptable Coding Unit (CU) structure with a specific end goal to expand the coding efficiency of all depth map characteristics. However, it results in an enormous Rate Distortion Optimization Cost (RDO-Cost) because of the broad recursive search for the best CU size from $$64\times 64$$ down to $$4\times 4$$ . This computational complexity excludes the 3D-HEVC from true and real-time application. Hence, it is imperative to build up an algorithm to diminish the complexity of the size decision in depth map intra-coding. To determine the previously mentioned issue, this paper proposes an effective 3D-HEVC PU size decision algorithm for depth map intra-video coding based on tensor features and statistical data analyses. The experimental results demonstrate that the proposed model diminishes the complexity of depth map size decision significantly with low rate distortion increase.
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