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Interweaved Prediction for Video Coding.

Authors :
Zhang, Kai
Zhang, Li
Liu, Hongbin
Xu, Jizheng
Deng, Zhipin
Wang, Yue
Source :
IEEE Transactions on Image Processing; 2020, Vol. 29, p6422-6437, 16p
Publication Year :
2020

Abstract

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%. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10577149
Volume :
29
Database :
Complementary Index
Journal :
IEEE Transactions on Image Processing
Publication Type :
Academic Journal
Accession number :
170078394
Full Text :
https://doi.org/10.1109/TIP.2020.2987432