Fast Depth Map Intra Coding for 3D Video Compression Based Tensor Feature Extraction and Data Analysis

Abstract

3D high-efficiency video coding (3D-HEVC) is the latest standard for 3D video compression created by the ISO/IEC MPEG and ITU-T Video Coding Experts Group (VCEG) based on a new video format called multiview texture videos plus depth maps (MVDs). In 3D-HEVC depth map intra prediction, the test model uses the conventional HEVC intra modes and new supplementary coding tools called depth modeling modes (DMMs) to better preserve the sharp edges of depth maps. These intra prediction modes fundamentally enhance the depth map intra coding efficiency. Although this process enables high coding efficiency, it also results in a very high encoding complexity, which limits the use of the 3D-HEVC encoder in practical and real-world applications. For most cases, the conventional intra coding and DMMs can be skipped if the current region is classified as a homogenous region. Bringing this intuitive approach to practice, this paper proposes fast depth map intra coding based on tensor feature extraction and data analysis. The experimental results show that the proposed intra model decision brings a good complexity reduction with negligible loss of rate distortion performance.