Depth modelling mode decision for depth intra coding via good feature

Chang Hong Fu, Ya Wen Zhao, Hong Bin Zhang, Yui Lam Chan, Wan Chi Siu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)


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.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781509021758
Publication statusPublished - 20 Feb 2018
Event24th IEEE International Conference on Image Processing, ICIP 2017 - China National Convention Center (CNCC), Beijing, China
Duration: 17 Sep 201720 Sep 2017


Conference24th IEEE International Conference on Image Processing, ICIP 2017


  • 3D-HEVC
  • Corner point
  • Depth map
  • Intra prediction
  • Multi-view video plus depth

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this