@inproceedings{8a1b971478034e7cafac927ff2f77be4,
title = "Study on Machine Learning Models for Tree Partitioning Method of Versatile Video Coding",
abstract = "In this paper, we propose a method using machine learning models to determine necessity of ternary tree (TT) partitioning in versatile video coding (VV C). To reduce the encoding complexity of a multi-type tree (MTT) partitioning in VVC, it is significantly important to early decide whether TT is needed. In this study, we analyze the correlation between the known features and the TT partitioning using extensive video dataset. We present a comparative study on machine learning models that consider the TT decision process as a binary classification problem. The experimental results show that the proposed model achieves higher accuracy than that of the existing model. Our code and dataset are available at https://github.com/sujineel/ICEIC_2022.",
keywords = "decision tree, encoding complexity, Machine learning, video compression, VVC",
author = "Sujin Lee and Park, {Sang Hyo}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022 ; Conference date: 06-02-2022 Through 09-02-2022",
year = "2022",
doi = "10.1109/ICEIC54506.2022.9748428",
language = "English",
series = "2022 International Conference on Electronics, Information, and Communication, ICEIC 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 International Conference on Electronics, Information, and Communication, ICEIC 2022",
address = "United States",
}