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Multimedia Tools and Applications

, Volume 78, Issue 6, pp 7819–7839 | Cite as

Fast CU size decision and PU mode decision algorithm for quality SHVC inter coding

  • Qiang Li
  • Bo LiuEmail author
  • Dayong Wang
Article
  • 86 Downloads

Abstract

As a scalable extension of the High Efficiency Video Coding (HEVC), the Scalable High Efficiency Video Coding (SHVC) encoder needs to encode multiple HEVC layers with Inter-layer predictions, which causes the significant increase in coding complexity. In this paper, we proposed a novel Inter prediction scheme to effectively reduce computational complexity in Quality SHVC. The new features of the proposed algorithm include: First, spatial and Inter-layer depth correlations are used to predict the most possible coding unit (CU) depth level candidates. Second, a statistical test method on the current CU depth level is introduced to examine whether the residual coefficients within its block present similar distribution to terminate depth selection early. Finally, during Inter prediction selection from 8 Prediction Unit (PU) sizes, spatial and Inter-layer correlations are combined with residual coefficients distribution to determine the PU partitioning mode is Symmetric Motion Partitioning (SMP) or Asymmetric Motion Partitioning (AMP). Experimental results demonstrate that the proposed algorithm can save an average of 65.33% coding time of enhancement layer (EL) while achieving a better rate distortion (RD) performance over other state-of-the-art work.

Keywords

SHVC CU size decision PU mode decision Inter prediction Low complexity compression 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61571071) and Nature Science Foundation Project of Chongqing (No. cstc2016jcyjA0543 and No. cstc2017jcyjXB0037). The authors also would like to thank all reviewers for their valuable comments and suggestions to improve the quality of this paper.

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Copyright information

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

Authors and Affiliations

  1. 1.Chongqing Key Laboratory of Signal and Information ProcessingChongqing University of Posts and TelecommunicationChongqingChina

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