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A Novel Fast Partition Algorithm Based on EDGE Information in HEVC

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Abstract

High efficiency video coding (HEVC) introduces a well-designed quad-tree structure to improve coding efficiency. During quad-tree building, the encoder tries all possible coding unit (CU) partition schemes to determine the optimal one with the minimum rate distortion cost, which introduces high computation complexity. To deal with this problem, a fast algorithm based on edge information is proposed to accelerate CU splitting in this paper. Firstly, the depth and prediction mode of adjacent blocks are obtained and used to facilitate rough partition. Secondly, the edge pixel values of this CU are counted and analyzed to fine-tune the partition. Experimental results show that the proposed algorithm can reduce coding time by 35.7% on average, with an acceptable loss of Bjøntegaard delta rate of 0.6%.

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Funding

This work is supported by the National Natural Science Foundation of China (no. 61370111), Beijing Municipal Natural Science Foundation (no. 4172020), Great Wall Scholar Project of Beijing Municipal Education Commission (CIT&TCD20180304), Beijing Youth Talent Project (CIT&TCD 201504001), and Beijing Municipal Education Commission General Program (KM201610009003).

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Contributions

All authors made substantial contributions to conception and design, analysis, and interpretation of data, and critical review of the manuscript. Zhi Liu carried out the idea and design of the study, and wrote the manuscript. Mengmeng Zhang got the funding for the study, and helped rewrite the manuscript substantially during the revision process. Delun Lai carried out the experiments, and performed the statistical analyses. Changzhi An optimized the experimental codes, and edited the manuscript.

Corresponding authors

Correspondence to Zhi Liu or Mengmeng Zhang.

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The authors declare that they have no conflicts of interest.

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Zhi Liu, Zhang, M., Lai, D. et al. A Novel Fast Partition Algorithm Based on EDGE Information in HEVC. Aut. Control Comp. Sci. 53, 471–479 (2019). https://doi.org/10.3103/S0146411619050110

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  • DOI: https://doi.org/10.3103/S0146411619050110

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