2026-03-12T13:35:13Z https://tokushima-u.repo.nii.ac.jp/oai

oai:tokushima-u.repo.nii.ac.jp:02011949 2025-04-28T06:24:55Z 1713853213384:1713853295607

Extreme Learning Machine-Enabled Coding Unit Partitioning Algorithm for Versatile Video Coding Jiang, Xiantao Xiang, Mo Jin, Jiayuan 宋, 天 ソウ, テン Song, Tian 宋 ソウ Song 天 テン Tian open access © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). versatile video coding coding unit extreme learning machine computation complexity The versatile video coding (VVC) standard offers improved coding efficiency compared to the high efficiency video coding (HEVC) standard in multimedia signal coding. However, this increased efficiency comes at the cost of increased coding complexity. This work proposes an efficient coding unit partitioning algorithm based on an extreme learning machine (ELM), which can reduce the coding complexity while ensuring coding efficiency. Firstly, the coding unit size decision is modeled as a classification problem. Secondly, an ELM classifier is trained to predict the coding unit size. In the experiment, the proposed approach is verified based on the VVC reference model. The results show that the proposed method can reduce coding complexity significantly, and good image quality can be obtained. MDPI 2023-09-07 eng journal article VoR 402920 https://tokushima-u.repo.nii.ac.jp/records/2011949 https://doi.org/10.3390/info14090494 10.3390/info14090494 20782489 Information 14 9 494 https://tokushima-u.repo.nii.ac.jp/record/2011949/files/info_14_9_494.pdf application/pdf 782 KB 2024-06-14