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oai:tokushima-u.repo.nii.ac.jp:02008737 2025-04-28T05:09:17Z 1713853213384:1713853295607

Spatial Correlation-Based Motion-Vector Prediction for Video-Coding Efficiency Improvement Jiang, Xiantao 10380130 宋, 天 ソウ, テン Song, Tian 宋 ソウ Song 天 テン Tian 70848522 片山, 貴文 カタヤマ, タカフミ Katayama, Takafumi 片山 カタヤマ Katayama 貴文 タカフミ Takafumi Leu, Jenq-Shiou open access This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). H.265/HEVC motion-vector prediction video-coding efficiency CU depth H.265/HEVC achieves an average bitrate reduction of 50% for fixed video quality compared with the H.264/AVC standard, while computation complexity is significantly increased. The purpose of this work is to improve coding efficiency for the next-generation video-coding standards. Therefore, by developing a novel spatial neighborhood subset, efficient spatial correlation-based motion vector prediction (MVP) with the coding-unit (CU) depth-prediction algorithm is proposed to improve coding efficiency. Firstly, by exploiting the reliability of neighboring candidate motion vectors (MVs), the spatial-candidate MVs are used to determine the optimized MVP for motion-data coding. Secondly, the spatial correlation-based coding-unit depth-prediction is presented to achieve a better trade-off between coding efficiency and computation complexity for interprediction. This approach can satisfy an extreme requirement of high coding efficiency with not-high requirements for real-time processing. The simulation results demonstrate that overall bitrates can be reduced, on average, by 5.35%, up to 9.89% compared with H.265/HEVC reference software in terms of the Bjontegaard Metric. MDPI 2019-01-23 eng journal article VoR 349175 https://tokushima-u.repo.nii.ac.jp/records/2008737 https://doi.org/10.3390/sym11020129 10.3390/sym11020129 20738994 Symmetry 11 2 129 https://tokushima-u.repo.nii.ac.jp/record/2008737/files/sym_11_2_129.pdf application/pdf 903 KB 2021-05-21