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IJMLC 2020 Vol.10(6): 777-782 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.6.1005

Fast CU Spliting Algorithms for Virtual Reality Video Based on KNN

Zhi Liu, Peiran Song, and Mengmeng Zhang

Abstract—The coding framework for virtual reality video at present is first projecting 3D data to 2D format, then encoding it by traditional coding tools, which has much high computational complexity. In order to reduce the coding complexity based on the quality evaluation standard of virtual reality video, this paper presents a fast algorithm to speed up the Coding Unit (CU) partitioning by predicting the maximum depth of LCU with KNN classifier. Experimental results show that the proposed fast algorithm provides an average time reduction rate of 37.9% compared to the reference HM-16.16+360lib4.0, with only 1.31% BD-rate increase.

Index Terms—Fast algorithm, LCU, KNN, virtual reality video.

The authors are with the North China University of Technology Beijing, China (e-mail: lzliu@ncut.edu.cn,18336343993@163.com, muchmeng@126.com).


Cite: Zhi Liu, Peiran Song, and Mengmeng Zhang, "Fast CU Spliting Algorithms for Virtual Reality Video Based on KNN," International Journal of Machine Learning and Computing vol. 10, no. 6, pp. 777-782, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net
  • APC: 500USD

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