图像压缩的最小二乘网格模型

Iciar Alvarez-Cascos, Yongyi Yang
{"title":"图像压缩的最小二乘网格模型","authors":"Iciar Alvarez-Cascos, Yongyi Yang","doi":"10.1109/ICIP.2004.1419488","DOIUrl":null,"url":null,"abstract":"In this work we explore the use of a content-adaptive mesh model for image compression. We first model the image to be compressed by a quadtree mesh representation, in which the nodal values are determined using a least squares fit. The resulting mesh structure is coded using a 4-ary tree and the mesh nodal values are coded using a hierarchical predictive coding scheme. Our experimental results demonstrate that the proposed approach can achieve good compression performance and can significantly outperform JPEG both subjectively and objectively in low bit-rate applications.","PeriodicalId":184798,"journal":{"name":"2004 International Conference on Image Processing, 2004. ICIP '04.","volume":"168 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Least-squares mesh model for image compression\",\"authors\":\"Iciar Alvarez-Cascos, Yongyi Yang\",\"doi\":\"10.1109/ICIP.2004.1419488\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work we explore the use of a content-adaptive mesh model for image compression. We first model the image to be compressed by a quadtree mesh representation, in which the nodal values are determined using a least squares fit. The resulting mesh structure is coded using a 4-ary tree and the mesh nodal values are coded using a hierarchical predictive coding scheme. Our experimental results demonstrate that the proposed approach can achieve good compression performance and can significantly outperform JPEG both subjectively and objectively in low bit-rate applications.\",\"PeriodicalId\":184798,\"journal\":{\"name\":\"2004 International Conference on Image Processing, 2004. ICIP '04.\",\"volume\":\"168 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2004 International Conference on Image Processing, 2004. ICIP '04.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIP.2004.1419488\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2004 International Conference on Image Processing, 2004. ICIP '04.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP.2004.1419488","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

在这项工作中,我们探索了使用内容自适应网格模型进行图像压缩。我们首先通过四叉树网格表示对要压缩的图像进行建模,其中节点值使用最小二乘拟合确定。所得的网格结构使用4元树编码,网格节点值使用分层预测编码方案编码。实验结果表明,在低比特率应用中,该方法可以获得良好的压缩性能,在主观上和客观上都明显优于JPEG。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Least-squares mesh model for image compression
In this work we explore the use of a content-adaptive mesh model for image compression. We first model the image to be compressed by a quadtree mesh representation, in which the nodal values are determined using a least squares fit. The resulting mesh structure is coded using a 4-ary tree and the mesh nodal values are coded using a hierarchical predictive coding scheme. Our experimental results demonstrate that the proposed approach can achieve good compression performance and can significantly outperform JPEG both subjectively and objectively in low bit-rate applications.
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
×
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:604180095
Book学术官方微信