Abstract
The current video coding standard HEVC has very high coding efficiency. However, its coding complexity is also very high, which leads to a negative impact on its wide applications. Therefore, how to improve the coding speed of HEVC has been a research focus recently. In this research, by applying machine learning methodology into video compression, we propose a novel fast intra coding algorithm for HEVC so as to improve the intra encoding speed. We first adopt Naive Bayesian to calculate each depth’s probability based on correlations. Then, to speed up coding, we combine textural features and possibilities with support vector machine (SVM) to further predict depth early skip and early termination. Experiments demonstrate that the proposed algorithm can significantly improve the coding speed with negligible loss of coding efficiency.
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Acknowledgments
This work was supported in part by Science and Technology Development Program of Central Guide to Local Government of China under Grant 2019ZYYD043, in part by International Science & Technology Cooperation Program of Hubei Province under Grant 2019AHB059, in part by Xiangyang Science and Technology Research and Development Project, in part by the Sichuan Science and Technology Program under Grants 2018RZ0072, 2019YFS0068 and 20ZDYF0660, in part by the Foundation of Chengdu University of Information Technology under Grant J201707, and in part by Open Project of Hubei University of Arts and Science under Grant XK2018013.
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Huang, Y., Wang, D., Sun, Y. et al. A fast intra coding algorithm for HEVC by jointly utilizing naive Bayesian and SVM. Multimed Tools Appl 79, 33957–33971 (2020). https://doi.org/10.1007/s11042-020-08882-x
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DOI: https://doi.org/10.1007/s11042-020-08882-x