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Interweaved Prediction for Video Coding.
- 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]
- Subjects :
- VIDEO compression
VIDEO coding
BLOCK codes
FORECASTING
STATISTICS
Subjects
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