Fast mode and depth decision for HEVC intra prediction based on edge detection and partition reconfiguration

Gaoxing Chen, Lei Sun, Zhenyu Liu, Takeshi Ikenaga

Research output: Contribution to journalArticlepeer-review


High efficiency video coding (HEVC) is a video compression standard that outperforms the predecessor H.264/AVC by doubling the compression efficiency. To enhance the intra prediction accuracy, 35 intra prediction modes were used in the prediction units (PUs), with partition sizes ranging from 4 × 4 to 64 × 64 in HEVC. However, the manifold prediction modes dramatically increase the encoding complexity. This paper proposes a fast mode-and depth-decision algorithm based on edge detection and reconfiguration to alleviate the large computational complexity in intra prediction with trivial degradation in accuracy. For mode decision, we propose pixel gradient statistics (PGS) and mode refinement (MR). PGS uses pixel gradient information to assist in selecting the prediction mode after rough mode decision (RMD). MR uses the neighboring mode information to select the best PU mode (BPM). For depth decision, we propose a partition reconfiguration algorithm to replace the original partitioning order with a more reasonable structure, by using the smoothness of the coding unit as a criterion in deciding the prediction depth. Smoothness detection is based on the PGS result. Experiment results show that the proposed method saves about 41.50% of the original processing time with little degradation (BD bitrate increased by 0.66% and BDPSNR decreased by 0.060 dB) in the coding gain.

Original languageEnglish
Pages (from-to)2130-2138
Number of pages9
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Issue number11
Publication statusPublished - 2014 Nov 1


  • Gradient
  • HEVC
  • Intra prediction
  • Mode decision
  • Partitioning

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics


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