Effective Data Driven Coding Unit Size Decision Approaches for HEVC INTRA Coding

Yun ZHANG, Zhaoqing PAN, Na LI, Xu WANG, Gangyi JIANG, Sam KWONG

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

51 Citations (Scopus)

Abstract

High Efficiency Video Coding (HEVC) INTRA coding improves compression efficiency by adopting advanced coding technologies, such as multi-level quad-tree block partitioning and up to 35-mode INTRA prediction. However, it significantly increases the coding complexity, memory access and power consumption, which goes against its widely applications, especially for ultra-high definition and/or mobile video applications. To tackle this problem, we propose an effective data driven Coding Unit (CU) size decision approaches for HEVC INTRA coding, which consists of two stages of Support Vector Machine based fast INTRA CU size decision schemes at four CU decision layers. At the first stage classification, a three output classifier with offline learning is developed to early terminate the CU size decision or early skip checking the current CU depth. As for the samples that neither early skipped nor early terminated, the second stage of binary classification, which learns online from previous coded frames, is proposed to further refine the CU size decision. Representative features for the CU size decision are explored at different decision layers and stages of classifications. Finally, the optimal parameters derived from the training data are achieved to reasonably allocate complexity among different CU layers at given total rate-distortion degradation constraint. Extensive experiments show that the proposed overall algorithm can achieve 27.95% to 80.53% and 52.48% on average complexity reduction for the CU size decision as compared with the original HM16.7 model. Meanwhile, the average Bjonteggard delta peak-signal-to-noise ratio degradation is only −0.08 dB, which is negligible. The overall performance of the proposed algorithm outperforms the state-of-the-art benchmark schemes.
Original languageEnglish
Pages (from-to)3208-3222
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume28
Issue number11
Early online date31 Aug 2017
DOIs
Publication statusPublished - Nov 2018
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 61471348 and Grant 61501246, in part by the Guangdong Natural Science Foundation for Distinguished Young Scholar under Grant 2016A030306022, in part by the National High-tech Research and Development Program of China under Grant 2015AA015901, in part by the Shenzhen Science and Technology Development Project under Grant JSGG20160229202345378, in part by the Shenzhen International Collaborative Research Project under Grant GJHZ20170314155404913, in part by the Ph.D. Start-Up Fund of the Guangdong Natural Science Foundation under Grant 2015A030310262, and in part by the Natural Science Foundation of Jiangsu Province under Grant BK20150930.

Keywords

  • CU size decision
  • High efficiency video coding
  • INTRA coding
  • machine learning

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