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Prediction complexity-based HEVC parallel processing for asymmetric multicores

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Abstract

This paper proposes a novel Tile allocation method considering the computational ability of asymmetric multicores as well as the computational complexity of each Tile. This paper measures the computational ability of asymmetric multicores in advance, and measures the computational complexity of each Tile by using the amount of HEVC prediction unit (PU) partitioning. The implemented system counts and sorts the amount of PU partitions of each Tile, and also allocates Tiles to asymmetric big.LITTLE cores according to their expected computational complexity. When experiments were conducted, the amount of PU partitioning and the computational complexity (decoding time) showed a close correlation, and average performance gains of decoding time with the proposed adaptive allocation were around 36 % with 12 Tiles, 28 % with 18 Tiles, and 31 % with 24 Tiles, respectively.

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Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2015R1C1A1A02037743).

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Authors and Affiliations

  1. Department of Computer Engineering, Gachon University, 1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, 13120, Republic of Korea

    Hyun-Joon Roh & Eun-Seok Ryu

  2. School of Industrial Management Engineering, Korea University, 145 Anam-Ro, Seongbuk-Gu, Seoul, 02841, Republic of Korea

    Sung Won Han

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  1. Hyun-Joon Roh
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  2. Sung Won Han
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Correspondence to Eun-Seok Ryu.

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Hyun-Joon Roh and Sung Won Han contributed equally to this work.

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Roh, HJ., Han, S.W. & Ryu, ES. Prediction complexity-based HEVC parallel processing for asymmetric multicores. Multimed Tools Appl 76, 25271–25284 (2017). https://doi.org/10.1007/s11042-017-4413-7

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  • DOI: https://doi.org/10.1007/s11042-017-4413-7

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