Spatio-Temporal Prediction Based Algorithm for Parallel Improvement of HEVC
Summary :
The next generation high efficiency video coding (HEVC) standard achieves high performance by extending the encoding block to 64×64. There are some parallel tools to improve the efficiency for encoder and decoder. However, owing to the dependence of the current prediction block and surrounding block, parallel processing at CU level and Sub-CU level are hard to achieve. In this paper, focusing on the spatial motion vector prediction (SMVP) and temporal motion vector prediction (TMVP), parallel improvement for spatio-temporal prediction algorithms are presented, which can remove the dependency between prediction coding units and neighboring coding units. Using this proposal, it is convenient to process motion estimation in parallel, which is suitable for different parallel platforms such as multi-core platform, compute unified device architecture (CUDA) and so on. The simulation experiment results demonstrate that based on HM12.0 test model for different test sequences, the proposed algorithm can improve the advanced motion vector prediction with only 0.01% BD-rate increase that result is better than previous work, and the BDPSNR is almost the same as the HEVC reference software.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E98-A No.11 pp.2229-2237
- Publication Date
- 2015/11/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E98.A.2229
- Type of Manuscript
- Special Section PAPER (Special Section on Smart Multimedia & Communication Systems)
- Category
Authors
Xiantao JIANG
Tongji University,Tokushima University
Tian SONG
Tokushima University
Takashi SHIMAMOTO
Tokushima University
Wen SHI
Tokushima University
Lisheng WANG
Tongji University
Keyword
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Xiantao JIANG, Tian SONG, Takashi SHIMAMOTO, Wen SHI, Lisheng WANG, "Spatio-Temporal Prediction Based Algorithm for Parallel Improvement of HEVC" in IEICE TRANSACTIONS on Fundamentals,
vol. E98-A, no. 11, pp. 2229-2237, November 2015, doi: 10.1587/transfun.E98.A.2229.
Abstract: The next generation high efficiency video coding (HEVC) standard achieves high performance by extending the encoding block to 64×64. There are some parallel tools to improve the efficiency for encoder and decoder. However, owing to the dependence of the current prediction block and surrounding block, parallel processing at CU level and Sub-CU level are hard to achieve. In this paper, focusing on the spatial motion vector prediction (SMVP) and temporal motion vector prediction (TMVP), parallel improvement for spatio-temporal prediction algorithms are presented, which can remove the dependency between prediction coding units and neighboring coding units. Using this proposal, it is convenient to process motion estimation in parallel, which is suitable for different parallel platforms such as multi-core platform, compute unified device architecture (CUDA) and so on. The simulation experiment results demonstrate that based on HM12.0 test model for different test sequences, the proposed algorithm can improve the advanced motion vector prediction with only 0.01% BD-rate increase that result is better than previous work, and the BDPSNR is almost the same as the HEVC reference software.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E98.A.2229/_p
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@ARTICLE{e98-a_11_2229,
author={Xiantao JIANG, Tian SONG, Takashi SHIMAMOTO, Wen SHI, Lisheng WANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Spatio-Temporal Prediction Based Algorithm for Parallel Improvement of HEVC},
year={2015},
volume={E98-A},
number={11},
pages={2229-2237},
abstract={The next generation high efficiency video coding (HEVC) standard achieves high performance by extending the encoding block to 64×64. There are some parallel tools to improve the efficiency for encoder and decoder. However, owing to the dependence of the current prediction block and surrounding block, parallel processing at CU level and Sub-CU level are hard to achieve. In this paper, focusing on the spatial motion vector prediction (SMVP) and temporal motion vector prediction (TMVP), parallel improvement for spatio-temporal prediction algorithms are presented, which can remove the dependency between prediction coding units and neighboring coding units. Using this proposal, it is convenient to process motion estimation in parallel, which is suitable for different parallel platforms such as multi-core platform, compute unified device architecture (CUDA) and so on. The simulation experiment results demonstrate that based on HM12.0 test model for different test sequences, the proposed algorithm can improve the advanced motion vector prediction with only 0.01% BD-rate increase that result is better than previous work, and the BDPSNR is almost the same as the HEVC reference software.},
keywords={},
doi={10.1587/transfun.E98.A.2229},
ISSN={1745-1337},
month={November},}
Copy
TY - JOUR
TI - Spatio-Temporal Prediction Based Algorithm for Parallel Improvement of HEVC
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2229
EP - 2237
AU - Xiantao JIANG
AU - Tian SONG
AU - Takashi SHIMAMOTO
AU - Wen SHI
AU - Lisheng WANG
PY - 2015
DO - 10.1587/transfun.E98.A.2229
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E98-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2015
AB - The next generation high efficiency video coding (HEVC) standard achieves high performance by extending the encoding block to 64×64. There are some parallel tools to improve the efficiency for encoder and decoder. However, owing to the dependence of the current prediction block and surrounding block, parallel processing at CU level and Sub-CU level are hard to achieve. In this paper, focusing on the spatial motion vector prediction (SMVP) and temporal motion vector prediction (TMVP), parallel improvement for spatio-temporal prediction algorithms are presented, which can remove the dependency between prediction coding units and neighboring coding units. Using this proposal, it is convenient to process motion estimation in parallel, which is suitable for different parallel platforms such as multi-core platform, compute unified device architecture (CUDA) and so on. The simulation experiment results demonstrate that based on HM12.0 test model for different test sequences, the proposed algorithm can improve the advanced motion vector prediction with only 0.01% BD-rate increase that result is better than previous work, and the BDPSNR is almost the same as the HEVC reference software.
ER -
English
