期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 24, 期 -, 页码 400-414出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2021.3052348
关键词
Fast coding algorithm; intra mode decision; new coding techniques; statistical learning; versatile video coding
资金
- National Natural Science Foundation of China [61931022, 61671282]
- Open Fund of Key Laboratory of Advanced Display, and System Applications of Ministry of Education (Shanghai University)
- Shanghai Science, and Technology Innovation Plan [18010500200]
- Shanghai Shuguang Program [17SG37]
This paper proposes a fast algorithm for Versatile Video Coding (VVC) to reduce coding complexity from the aspects of mode selection and prediction terminating. The algorithm removes non-promising modes using adaptive mode pruning (AMP) and improves efficiency by sorting candidate modes with an ensemble decision strategy. It also selects an appropriate model through mode-dependent termination (MDT) to terminate unnecessary intra predictions.
To achieve higher coding efficiency, the latest Versatile Video Coding (VVC) standard adopts a series of new intra coding techniques, including the quadtree plus multi-type tree (QTMT), intra sub-partitions (ISP) and intra block copy (IBC). However, this makes the intra codingmore complicated, asVVCneeds to traverse all prediction modes and partition types of QTMT to find the optimal combination. In this paper, we propose a fast algorithm for VVCfromtwo aspects ofmode selection and prediction terminating to reduce coding complexity. For the mode selection, adaptive mode pruning (AMP) is proposed to remove non-promisingmodes. First, since the newly introduced modes (IBC and ISP) are not effective for all blocks, learning-based classifiers are designed to remove them intelligently. Second, for normal modes, an ensemble decision strategy is proposed to sort the candidate modes and increase the probability of being the optimal mode for the first few candidates; thus, we can remove redundant candidates more efficiently. In terms of prediction terminating, we find that different optimal modes of current depth level lead to different termination probabilities of remaining intra predictions. Therefore, modedependent termination (MDT) is proposed to select an appropriate model through the optimal mode and terminate unnecessary intra predictions of remaining depth levels. The proposed algorithm is implemented on VVC test model, and simulation results show that it can achieve 51%similar to 53% time savings with only 0.93%similar to 1.08% BDBR increases.
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