Binary and multi-class learning based low complexity optimization for HEVC encoding

Linwei ZHU, Yun ZHANG, Zhaoqing PAN, Ran WANG, Sam KWONG, Zongju PENG

Research output: Journal PublicationsJournal Article (refereed)peer-review

73 Citations (Scopus)

Abstract

High Efficiency Video Coding (HEVC) improves the compression efficiency at the cost of high computational complexity by using the quad-tree coding unit (CU) structure and variable prediction unit (PU) modes. To minimize the HEVC encoding complexity while maintaining its compression efficiency, a binaryand multi-class support vector machine (SVM)-based fast HEVC encoding algorithm is presented in this paper. First, the processes of recursive CU decision and PU selection in HEVC are modeled as hierarchical binary classification and multi-class classification structures. Second, according to the two classification structures, the CU decision and PU selection are optimized by binary and multi-class SVM, i.e., the CU and PU can be predicted directly via classifiers without intensive rate distortion (RD) cost calculation. In particular, to achieve better prediction performance, a learning method is proposed to combine the off-line machine learning (ML) mode and on-line ML mode for classifiers based ona multiple reviewers system. Additionally, the optimal parameters determination scheme is adopted for flexible complexity allocation under a given RD constraint. Experimental results show that the proposed method can achieve 68.3%, 67.3%, and 65.6% time saving on average while the values of Bjøntegaard delta peaksignal-to-noise ratio are −0.093 dB, −0.091 dB, and −0.094 dB and the values of Bjøntegaard delta bit rate are 4.191%, 3.842%, and 3.665% under low delay P main, low delay main, and random access configurations, respectively, when compared with the HEVC test model version HM 16.5. Meanwhile, the proposed.
Original languageEnglish
Pages (from-to)547-561
JournalIEEE Transactions on Broadcasting
Volume63
Issue number3
Early online date23 Jun 2017
DOIs
Publication statusPublished - 1 Sept 2017
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 61471348, Grant 61672443, and Grant 61402460, in part by the Guangdong Natural Science Funds for Distinguished Young Scholar under Grant 2016A030306022, in part by the Project for Shenzhen Science and Technology Development under Grant JSGG20160229202345378, and in part by the Hong Kong RGC General Research Fund under Grant 9042322 (CityU 11200116).

Keywords

  • Coding unit (CU)
  • High efficiency video coding (HEVC)
  • Multi-class learning
  • Multiple reviewers system
  • Prediction unit (PU)
  • Support vector machine (SVM)

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