期刊
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
卷 31, 期 10, 页码 4096-4106出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2020.3043005
关键词
Complexity theory; Encoding; Optimization; Resource management; Image coding; Regulation; Computational complexity; Video coding; Multiview High Efficiency Video Coding (MV-HEVC); complexity optimization
资金
- National Natural Science Foundation of China [61671152]
- Natural Science Foundation of Fujian Province [2019J01222]
This research proposes a flexible complexity optimization framework for addressing the complexity issue in multiview video encoding. The algorithm reduces encoder complexity at different external-defined complexity constraints and is committed to compression efficiency optimization at different complexity levels.
Flexible optimization on video coding computational complexity is imperative to adapt to diverse terminals in video streaming, especially with high definition videos and increasingly comprehensive encoders. Until now, few efforts have been contributed to the encoder of multiview videos, which complicates its encoding structure with duplex frame display. To address this issue, we propose a flexible complexity optimization framework in this paper. In the first place, the proposed algorithm flexibly reduces the encoder complexity at different external-defined complexity constraints; furthermore, it is also committed to compression efficiency optimization at different complexity levels. The framework is achieved by a hybrid approach: complexity allocation, which allocates the external-defined complexity constraint to all views, hierarchical layers and frames based on linear programming; and complexity regulation, which dynamically adjusts local candidate partitions to fulfil the targeted local complexity constraint, with a probability-driven Alternate Partition Cost (APC) minimization. The overall algorithm is implemented on the popular multiview encoder, Multiview High Efficiency Video Coding (MV-HEVC), with promising Rate-Distortion (RD) performances at different complexity constraints, which are also superior to two recent complexity optimization algorithms.
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