@inproceedings{b7272b2b19c64bbf9b2f20232aa14c87,
title = "Machine Learning Based Efficient Qt-Mtt Partitioning for VVC Inter Coding",
abstract = "The Joint Video Experts Team (JVET) have standardized the Versatile Video Coding (VVC) in 2020 targeting efficient coding of the emerging video services and formats such as 8K and immersive video streaming applications. VVC standard enhances the coding efficiency by 40% at the cost of an encoder computational complexity increase estimated to 859%(x8) compared to the previous standard High Efficiency Video Coding (HEVC). This work aims at reducing the complexity of the VVC encoder under the Random Access (RA) configuration. The proposed method takes advantage of the inter prediction in order to predict the split probabilities through a convolutional neural network. Our solution reaches 31.8% of complexity reduction for a negligible bitrate increase of 1.11% outperforming state-of-the-art methods.",
keywords = "Image resolution, Limiting, Neural networks, Machine learning, Streaming media, Encoding, Complexity theory, Versatile Video Coding (VVC), complexity reduction, Machine Learning (ML), Convolutional Neural Network (CNN), Decision Tree (DT)",
author = "A. Tissier and W. Hamidouche and J. Vanne and D. Menard",
note = "jufoid=57423; IEEE International Conference on Image Processing ; Conference date: 16-10-2022 Through 19-10-2022",
year = "2022",
doi = "10.1109/ICIP46576.2022.9898052",
language = "English",
series = "Proceedings : International Conference on Image Processing",
publisher = "IEEE",
pages = "1401--1405",
booktitle = "2022 IEEE International Conference on Image Processing (ICIP)",
}