Optimal Stopping Theory-Enabled VVC Intra Prediction with Texture

Yucheng Li, Xiantao Jiang, Wei Li, Jiayuan Jin, Dezhi Han, Tian Song, Fei Yu
{"title":"Optimal Stopping Theory-Enabled VVC Intra Prediction with Texture","authors":"Yucheng Li, Xiantao Jiang, Wei Li, Jiayuan Jin, Dezhi Han, Tian Song, Fei Yu","doi":"10.1109/CCISP55629.2022.9974416","DOIUrl":null,"url":null,"abstract":"Versatile Video Coding (VVC) introduces the new quad-tree with a nested multi-type tree (QTMT) block division structure, which increases the flexibility of block division, the more complex block division structure increases the coding complexity of VVC by nearly 26 times compared with High-Efficiency Video Coding (HEVC). Therefore, it is urgent to reduce the coding complexity of VVC. In this paper, we propose a fast CU division method based on optimal stopping theory and block texture decision. Firstly, by analyzing the division depth of the Coding Tree Unit (CTU) at the same position as neighboring frames, we use the optimal stopping theory to determine the optimal division layer of the current CTU, to terminate the division process in advance. Then, by judging the texture direction of the current Coding Unit (CU), the calculation of several CU division methods is selected to be skipped, thus reducing the computational effort of coding. The experimental results show that the coding time of this scheme is reduced by 45.65% on average, while the BDBR only increases by 1.64%.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCISP55629.2022.9974416","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Versatile Video Coding (VVC) introduces the new quad-tree with a nested multi-type tree (QTMT) block division structure, which increases the flexibility of block division, the more complex block division structure increases the coding complexity of VVC by nearly 26 times compared with High-Efficiency Video Coding (HEVC). Therefore, it is urgent to reduce the coding complexity of VVC. In this paper, we propose a fast CU division method based on optimal stopping theory and block texture decision. Firstly, by analyzing the division depth of the Coding Tree Unit (CTU) at the same position as neighboring frames, we use the optimal stopping theory to determine the optimal division layer of the current CTU, to terminate the division process in advance. Then, by judging the texture direction of the current Coding Unit (CU), the calculation of several CU division methods is selected to be skipped, thus reducing the computational effort of coding. The experimental results show that the coding time of this scheme is reduced by 45.65% on average, while the BDBR only increases by 1.64%.
最优停止理论支持的纹理VVC内预测
通用视频编码(VVC)引入了一种新的四叉树嵌套多类型树(QTMT)块划分结构,增加了块划分的灵活性,更复杂的块划分结构使VVC的编码复杂度比高效视频编码(HEVC)提高了近26倍。因此,降低VVC的编码复杂度是当务之急。本文提出了一种基于最优停止理论和块纹理判定的快速CU分割方法。首先,通过分析相邻帧在同一位置的编码树单元(CTU)的分割深度,利用最优停止理论确定当前CTU的最优分割层,提前终止分割过程;然后,通过判断当前编码单元(Coding Unit, CU)的纹理方向,选择几种CU划分方法的计算跳过,从而减少编码的计算量。实验结果表明,该方案的编码时间平均缩短了45.65%,而BDBR仅增加了1.64%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信