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Interweaved Prediction for Video Coding

delete2020-01-01
delete6
PRE
AI
K
Kai Zhang
L
Li Zhang *
H
Hongbin Liu
J
Jizheng Xu
Z
Zhipin Deng
王玥 cover
王玥 (Yue Wang)
DOI:10.1109/TIP.2020.2987432delete
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Abstract

Abstract

En 中文
In the emerging next generation video coding standard Versatile Video Coding (VVC) developed by the Joint Video Exploration Team (JVET), sub-block-based inter-prediction plays a key role in promising coding tools such as Affine Motion Compensation (AMC) and sub-block-based Temporal Motion Vector Prediction (sbTMVP). With sub-block-based inter-prediction, a coding block is divided into sub-blocks, and the motion information of each sub-block is derived individually. Although sub-block-based inter-prediction can provide a higher quality prediction benefiting from a finer motion granularity, it still suffers two problems: uneven prediction quality and boundary discontinuity. In this paper, we present a method of interweaved prediction to further improve sub-block-based inter-prediction. With interweaved prediction, a coding block with AMC or sbTMVP mode is divided into sub-blocks with two different dividing patterns, so that a corner position of a sub-block in one dividing pattern coincides with the central position of a sub-block in the other dividing pattern. Then two auxiliary predictions are generated by AMC or sbTMVP with the two dividing patterns, independently. The final prediction is calculated as a weighted-sum of the two auxiliary predictions. Theoretical analysis and statistical data prove that interweaved prediction can significantly mitigate the two problems in sub-block-based inter-prediction. Simulation results show that the proposed methods can achieve 0.64% BD-rate saving on average with the random access configurations. On sequences with rich affine motions, the average BD-rate saving can be up to 2.54%.
Keywords:
Video coding
Image coding
Complexity theory
Adaptation models
Tools
Motion compensation
Encoding
Video coding
sub-block
affine
AMC
sbTMVP
interweaved prediction
VVC
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Journal

IEEE Transactions on Image Processing cover
IEEE Transactions on Image Processing
IF:
13.7
Papers:
1.0W
Citations:
8.4W

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