Depth modelling mode decision for depth intra coding via good feature

Abstract

The depth modelling modes (DMM) and 35 conventional intra modes (CHIMs) introduced in 3D-HEVC results in unacceptable huge complexity of depth intra coding. However, some redundancy between DMM and CHIMs could be avoided to accelerate the process. In this paper, a good feature-corner point (CP) is proposed to evaluate the orientation of edge in a given prediction unit (PU), by which a binary classifier is created. We further investigate the probability distribution of DMM, which is selected as the optimal intra mode in each category. According to the statistical analysis, the skipping of DMM decision is proposed to eliminate the cases which have been predicted well by CHIMs. The experimental results show that, compared with the test model HTM-13.0 of 3D-HEVC, the proposed algorithm can yield about 17% time reduction for depth intra coding with almost no degradation in coding performance.