New relationships in operator-based backward motion compensation

Aria Nosratinia, M. Orchard
{"title":"New relationships in operator-based backward motion compensation","authors":"Aria Nosratinia, M. Orchard","doi":"10.1109/DCC.1995.515529","DOIUrl":null,"url":null,"abstract":"The transmission and storage of digital video at reduced bit rates requires a source coding scheme, which generally contains motion compensated prediction as an essential part. The class of motion estimation algorithms known as backward methods have the advantage of dense motion field sampling, and in coding applications the decoder needs no motion information from the coder. In this paper, we first present an overview of operator based motion compensators with interpolative and non-interpolative kernels. We then proceed with two new results. The first offers a new perspective on the classical pel-recursive methods; one that exposes the weaknesses of traditional approaches and offers an explanation for the improved performance of operator-based algorithms. The second result introduces a minimum norm intra-frame operator and establishes an equivalence relationship between this and the original (least squares) operator. This equivalence induces interesting duality properties that, in addition to offering insights into operator-based motion estimators, can be used to relax either the maximum needed computational power or the frame buffer length.","PeriodicalId":107017,"journal":{"name":"Proceedings DCC '95 Data Compression Conference","volume":"504 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings DCC '95 Data Compression Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DCC.1995.515529","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

The transmission and storage of digital video at reduced bit rates requires a source coding scheme, which generally contains motion compensated prediction as an essential part. The class of motion estimation algorithms known as backward methods have the advantage of dense motion field sampling, and in coding applications the decoder needs no motion information from the coder. In this paper, we first present an overview of operator based motion compensators with interpolative and non-interpolative kernels. We then proceed with two new results. The first offers a new perspective on the classical pel-recursive methods; one that exposes the weaknesses of traditional approaches and offers an explanation for the improved performance of operator-based algorithms. The second result introduces a minimum norm intra-frame operator and establishes an equivalence relationship between this and the original (least squares) operator. This equivalence induces interesting duality properties that, in addition to offering insights into operator-based motion estimators, can be used to relax either the maximum needed computational power or the frame buffer length.
基于算子的反向运动补偿中的新关系
低比特率数字视频的传输和存储需要一种源编码方案,而源编码方案通常包含运动补偿预测作为其重要组成部分。运动估计算法被称为反向方法,它具有密集运动场采样的优点,并且在编码应用中,解码器不需要来自编码器的运动信息。在本文中,我们首先概述了基于算子的运动补偿器的插值和非插值核。然后我们得到两个新的结果。第一种方法提供了对经典递归方法的新视角;它揭示了传统方法的弱点,并为基于算子的算法的性能改进提供了解释。第二种结果引入了最小范数帧内算子,并建立了该算子与原始(最小二乘)算子之间的等价关系。这种等价性引出了有趣的对偶性,除了提供对基于算子的运动估计器的见解之外,还可以用来放松所需的最大计算能力或帧缓冲长度。
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