Improved Block Matching Motion Estimation Technique using Modified Particle Swarm Optimization in Video Coding

Deepak Singh
{"title":"Improved Block Matching Motion Estimation Technique using Modified Particle Swarm Optimization in Video Coding","authors":"Deepak Singh","doi":"10.1109/ETI4.051663.2021.9619265","DOIUrl":null,"url":null,"abstract":"In video coding, the block matching based motion estimation (BMME) schemes by exploiting the uni-modal error surface is quite popular for easy implementation. Though, many real-world video sequences exhibit multiple local minima within the block search window. This research article proposes a pattern based modified particle swarm optimization approach for motion estimation (PMPSO-ME). The population-based evolutionary method; PSO ensures the global optimum solution and avoids trapping into local minima.The proposed technique is analyzed on JM 18.6 reference software of H.264/AVC platform. The proposed technique is evaluated for compared with the state-of-the-art approaches in terms of number of search points, encoding time complexity, Bjontegaard metrics etc.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETI4.051663.2021.9619265","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

In video coding, the block matching based motion estimation (BMME) schemes by exploiting the uni-modal error surface is quite popular for easy implementation. Though, many real-world video sequences exhibit multiple local minima within the block search window. This research article proposes a pattern based modified particle swarm optimization approach for motion estimation (PMPSO-ME). The population-based evolutionary method; PSO ensures the global optimum solution and avoids trapping into local minima.The proposed technique is analyzed on JM 18.6 reference software of H.264/AVC platform. The proposed technique is evaluated for compared with the state-of-the-art approaches in terms of number of search points, encoding time complexity, Bjontegaard metrics etc.
基于改进粒子群优化的视频编码块匹配运动估计技术
在视频编码中,利用单模态误差曲面的基于块匹配的运动估计(BMME)方案以其易于实现而广受欢迎。然而,许多真实世界的视频序列在块搜索窗口中表现出多个局部最小值。提出了一种基于模式的改进粒子群优化运动估计方法(PMPSO-ME)。基于种群的进化方法;粒子群算法保证了全局最优解,避免陷入局部最小值。在H.264/AVC平台的JM 18.6参考软件上对该技术进行了分析。从搜索点数量、编码时间复杂度、比昂特加德度量等方面对该方法进行了评价。
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