Lossless computational reduction of full search algorithm in motion estimation using appropriate matching unit from image localization

Jong-Nam Kim, Byung-Ha Ahn
{"title":"Lossless computational reduction of full search algorithm in motion estimation using appropriate matching unit from image localization","authors":"Jong-Nam Kim, Byung-Ha Ahn","doi":"10.1109/ITCC.2001.918837","DOIUrl":null,"url":null,"abstract":"To reduce the amount of computation of the full search (FS) algorithm for fast motion estimation, we propose a new and fast matching algorithm without degradation of predicted images as in conventional FS. The computational reduction without any degradation in the predicted image comes from fast removal of impossible motion vectors. We obtain faster removal of inappropriate motion vectors using efficient matching units from the localization of complex area in image data. In this paper, we show three properties in block matching of motion estimation. Experimentally, we reduce unnecessary computations by about 30% with our algorithm compared with the conventional fast matching scan algorithm.","PeriodicalId":318295,"journal":{"name":"Proceedings International Conference on Information Technology: Coding and Computing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings International Conference on Information Technology: Coding and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITCC.2001.918837","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

To reduce the amount of computation of the full search (FS) algorithm for fast motion estimation, we propose a new and fast matching algorithm without degradation of predicted images as in conventional FS. The computational reduction without any degradation in the predicted image comes from fast removal of impossible motion vectors. We obtain faster removal of inappropriate motion vectors using efficient matching units from the localization of complex area in image data. In this paper, we show three properties in block matching of motion estimation. Experimentally, we reduce unnecessary computations by about 30% with our algorithm compared with the conventional fast matching scan algorithm.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于图像定位的合适匹配单元的运动估计全搜索算法的无损计算缩减
为了减少全搜索(FS)算法在快速运动估计中的计算量,我们提出了一种新的快速匹配算法,该算法不会像传统的FS算法那样降低预测图像的质量。在预测图像中没有任何退化的计算量减少来自于快速去除不可能的运动向量。我们利用图像数据中复杂区域定位的有效匹配单元,更快地去除不适当的运动向量。本文给出了运动估计中块匹配的三个特性。实验结果表明,与传统的快速匹配扫描算法相比,该算法减少了约30%的不必要的计算量。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Advanced research issues for tomorrow's multimedia networks Adaptive thresholding of document images based on Laplacian sign Special sessions on multimedia security and watermarking applications Content-based retrieval and data mining of a skin cancer image database Building perception for scheduling and executing a task using multi-agent systems
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1