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Multimedia Tools and Applications

, Volume 78, Issue 1, pp 289–310 | Cite as

Adaptive intra mode decision for HEVC based on texture characteristics and multiple reference lines

  • Rui Tian
  • Yongfei ZhangEmail author
  • Miyi Duan
  • Xi Li
Article
  • 77 Downloads

Abstract

The High Efficiency Video Coding (HEVC) standard was designed to achieve significantly improved coding efficiency compared with the widespread use of H.264/AVC standards. This achievement was motivated by the ever-increasing popularity of high-definition and ultra-HD video application. However, this comes at the expense of a significant increase in encoder complexity, especially in intra-frame coding. To enhance the intra coding performance, a set of 35 intra prediction modes is adopted in HEVC. To reduce the complexity of intra prediction while maintaining the coding performance, an adaptive fast mode decision algorithm for HEVC intra coding based on texture characteristics and multiple reference lines is proposed in this paper. First, we take advantage of pixel values deviation (PVD) to obtain dominate texture direction of prediction unit (PU) and predict the texture prediction direction candidate set out of all 35 intra prediction modes based on texture direction with due consideration of texture complexity and PU size. Second, an adaptive multiple reference line-based intra prediction scheme will be utilized with classification strategy to improve coding efficiency. Third, the relation observed between the costs of two candidate modes will be exploited to improve the efficiency of prediction. Experimental results demonstrate that the proposed algorithm saves 20.45% intra encoding time on average without incurring noticeable performance degradation and outperforms the state-of-the-art intra mode decision algorithms by achieving a better RD performance with approximate encoding time saving.

Keywords

HEVC Intra mode decision Texture characteristics Multiple reference lines Adaptive threshold 

Notes

Acknowledgments

This work was partially supported by the National Key Research and Development Plan (Grant No. 2016YFC0801001), the NSFC Key Project (No. 61632001) and the National Natural Science Foundation of China (No. 61772054).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Digital MediaBeihang UniversityBeijingChina
  2. 2.Beijing Key Laboratory of Digital MediaSchool of Computer Science and Engineering and the State Key Laboratory of Virtual Reality Technology and SystemsBeijingChina
  3. 3.Institute of Computing TechnologyChinese Academy of SciencesBeijingPeople’s Republic of China

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