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

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

73 Scopus Citations
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Author(s)

  • Yun Zhang
  • Zhaoqing Pan
  • Ran Wang
  • Zongju Peng

Detail(s)

Original languageEnglish
Article number2711142
Pages (from-to)547-561
Journal / PublicationIEEE Transactions on Broadcasting
Volume63
Issue number3
Online published23 Jun 2017
Publication statusPublished - 1 Sept 2017

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 binary
and 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 on
a 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 peak
signal-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.

Research Area(s)

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

Citation Format(s)

Binary and multi-class learning based low complexity optimization for HEVC encoding. / Zhu, Linwei; Zhang, Yun; Pan, Zhaoqing et al.
In: IEEE Transactions on Broadcasting, Vol. 63, No. 3, 2711142, 01.09.2017, p. 547-561.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review