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HEVC coding unit decision based on machine learning

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Abstract

The high-efficiency video coding standard (HEVC) coding efficiency performance is mainly reached due to the recursive quad-tree coding unit mode decision tool. Nevertheless, this sophisticated module impact on enhancing coding efficiency came at the expense of noticeable computational complexity increase. To speed up the encoding process, an efficient fast coding unit (CU) decision algorithm based on fuzzy support vector machine (FSVM) is adopted in this paper. The purpose of this approach is to predict coding decision without RD cost calculation, which eliminates extensive computational time used to check all block decision candidates. An appropriate feature set is first selected to achieve excellent accuracy performance. Then, trained classifiers are incorporated into reference encoder and combined with fast mode decision algorithms to further improve the coding efficiency. Proposed model exhibits high classification accuracy in CU partitioning. Experimental results show a significant speedup in terms of encoding time reaching 70.57% with 0.22% compression efficiency improvement at the same objective video quality.

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Authors and Affiliations

  1. Electronic and Information Technology Laboratory, University of Sfax, Sfax, Tunisia

    Imen Werda, Fatma Belghith & Nouri Masmoudi

  2. Electronics and Microelectronics Laboratory, University of Monastir, Monastir, Tunisia

    Amna Maraoui

  3. Networked Objects, Control and Communication Systems, University of Sousse, Sousse, Tunisia

    Ezahra Fatma Sayadi

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  1. Imen Werda
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  2. Amna Maraoui
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  3. Fatma Belghith
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  4. Ezahra Fatma Sayadi
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  5. Nouri Masmoudi
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Werda, I., Maraoui, A., Belghith, F. et al. HEVC coding unit decision based on machine learning. SIViP 16, 1345–1353 (2022). https://doi.org/10.1007/s11760-021-02086-y

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  • DOI: https://doi.org/10.1007/s11760-021-02086-y

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