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Self-learning residual model for fast intra CU size decision in 3D-HEVC

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成果类型:
期刊论文
作者:
YueLi;NingboZhu;GaoboYang;YapeiZhu;XianglingDing
作者机构:
Computer School, University of South China, Hengyang, 421001, China
School of Information Science and Engineering, Hunan University, Changsha, 410082, China
Faculty of Physics and Electronic Information Science, Hengyang Normal University, Hengyang, 421002, China
School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China
语种:
中文
关键词:
3D high efficiency video coding;Self-learning;Fast intra coding;CU size decision;Residual signal
期刊:
Signal Processing: Image Communication
ISSN:
0923-5965
年:
2020
卷:
80
期:
Volume 80
页码:
115660-
机构署名:
本校为其他机构
院系归属:
物理与电子工程学院
摘要:
As an extension of the High Efficiency Video Coding (HEVC) standard, 3D-HEVC requires to encode multiple texture views and depth maps, which inherits the same quad-tree coding structure as HEVC. Due to the distinct properties of texture views and depth maps, existing fast intra prediction approaches were presented for the coding of texture views and depth maps, respectively. To further reduce the coding complexity of 3D-HEVC, a self-learning residual model-based fast coding unit (CU) size decision approach is proposed for the intra coding of both texture views and depth maps. Residual signal, which is defined as the difference between the original luminance pixel and the optimal prediction luminance pixel, is firstly extracted from each CU. Since residue signal is strongly correlated with the optimal CU partition, it is used as the feature of each CU. Then, a self-learning residual model is established by intra feature learning, which iteratively learns the features of the previously encoded coding tree unit (CTU) generated by itself. Finally, a binary classifier is developed with the self-learning residual model to early terminate CU size decision of both texture views and depth maps. Experimental results show the proposed fast intra CU size decision approach achieves 33.3% and 49.3% encoding time reduction on average for texture views and depth maps with negligible loss of overall video quality, respectively.

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