基于修正多重三角不等式的矢量量化快速搜索算法

C. H. Wu, Hsung-Pin Chang, Y. C. Liu, G. H. Lee, L. Chi
{"title":"基于修正多重三角不等式的矢量量化快速搜索算法","authors":"C. H. Wu, Hsung-Pin Chang, Y. C. Liu, G. H. Lee, L. Chi","doi":"10.1109/TAAI.2016.7880112","DOIUrl":null,"url":null,"abstract":"This paper proposes a Modified Multiple Triangular Inequality Elimination (MMTIE) to further reduce the search number of candidate codevectors. The MMTIE adopts the same original search space as the MTIE scheme with the initial best-matched codevector selected by the Initial Index Code Assignment (IICA), and integrates the intersection rule of the Candidate Codevectors Group (CCG) scheme to further reduce the search space and find the best-matched candidate by table look-up operation in the coding stage. Since the IICA approach selects an initial best-matched codevector by exploiting the correlations of the neighboring blocks and the predefined CCG space is obtained from the off-line stage, the MMTIE algorithm achieves better coding efficiency than the original MTIE at a cost of extra memory. In addition, the proposed algorithm provides the same coding quality as the full search method. Experimental results demonstrate the effectiveness of the proposed scheme in comparison with our previous MTIE coding scheme.","PeriodicalId":159858,"journal":{"name":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fast-searching algorithm for vector quantization using modified multiple triangular inequality\",\"authors\":\"C. H. Wu, Hsung-Pin Chang, Y. C. Liu, G. H. Lee, L. Chi\",\"doi\":\"10.1109/TAAI.2016.7880112\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a Modified Multiple Triangular Inequality Elimination (MMTIE) to further reduce the search number of candidate codevectors. The MMTIE adopts the same original search space as the MTIE scheme with the initial best-matched codevector selected by the Initial Index Code Assignment (IICA), and integrates the intersection rule of the Candidate Codevectors Group (CCG) scheme to further reduce the search space and find the best-matched candidate by table look-up operation in the coding stage. Since the IICA approach selects an initial best-matched codevector by exploiting the correlations of the neighboring blocks and the predefined CCG space is obtained from the off-line stage, the MMTIE algorithm achieves better coding efficiency than the original MTIE at a cost of extra memory. In addition, the proposed algorithm provides the same coding quality as the full search method. Experimental results demonstrate the effectiveness of the proposed scheme in comparison with our previous MTIE coding scheme.\",\"PeriodicalId\":159858,\"journal\":{\"name\":\"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)\",\"volume\":\"114 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TAAI.2016.7880112\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TAAI.2016.7880112","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

为了进一步减少候选编码向量的搜索次数,本文提出了一种改进的多重三角不等式消除算法(MMTIE)。MMTIE采用与MTIE方案相同的原始搜索空间,采用初始索引码分配(initial Index Code Assignment, IICA)选择的初始最匹配码向量,并融合候选码向量群(Candidate codevector Group, CCG)方案的交集规则,进一步缩小搜索空间,在编码阶段通过表查找操作找到最匹配的候选码向量。由于IICA方法通过利用相邻块的相关性来选择初始最匹配的编码向量,并且从离线阶段获得预定义的CCG空间,因此MMTIE算法以额外的内存为代价获得了比原始MTIE更好的编码效率。此外,该算法具有与全搜索方法相同的编码质量。实验结果表明,该方案与之前的MTIE编码方案相比是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Fast-searching algorithm for vector quantization using modified multiple triangular inequality
This paper proposes a Modified Multiple Triangular Inequality Elimination (MMTIE) to further reduce the search number of candidate codevectors. The MMTIE adopts the same original search space as the MTIE scheme with the initial best-matched codevector selected by the Initial Index Code Assignment (IICA), and integrates the intersection rule of the Candidate Codevectors Group (CCG) scheme to further reduce the search space and find the best-matched candidate by table look-up operation in the coding stage. Since the IICA approach selects an initial best-matched codevector by exploiting the correlations of the neighboring blocks and the predefined CCG space is obtained from the off-line stage, the MMTIE algorithm achieves better coding efficiency than the original MTIE at a cost of extra memory. In addition, the proposed algorithm provides the same coding quality as the full search method. Experimental results demonstrate the effectiveness of the proposed scheme in comparison with our previous MTIE coding scheme.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A cluster-based opinion leader discovery in social network User behavior analysis and commodity recommendation for point-earning apps Extraction of proper names from myanmar text using latent dirichlet allocation Heuristic algorithm for target coverage with connectivity fault-tolerance problem in wireless sensor networks AFIS: Aligning detail-pages for full schema induction
×
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:481959085
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