Image resizing in the compressed domain

J. Mukhopadhyay
{"title":"Image resizing in the compressed domain","authors":"J. Mukhopadhyay","doi":"10.1109/ISSCS.2017.8034942","DOIUrl":null,"url":null,"abstract":"Image resizing is used to convert an image of a given size to one of a different size. There exist different algorithms for resizing an image both in the spatial domain, as well as in the frequency domain where it is stored in compressed form. There are certain advantages of performing the resizing operation directly in the compressed domain. First, it saves the computational overhead of inverse and forward transforms. Next, by exploiting various properties of the transform domain, it is possible to design efficient fast algorithms providing good quality reconstructed image in the spatial domain. In this paper, we review a few algorithms for resizing an image by arbitrary sizes and provide a brief comparison of their performances.","PeriodicalId":338255,"journal":{"name":"2017 International Symposium on Signals, Circuits and Systems (ISSCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Symposium on Signals, Circuits and Systems (ISSCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSCS.2017.8034942","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Image resizing is used to convert an image of a given size to one of a different size. There exist different algorithms for resizing an image both in the spatial domain, as well as in the frequency domain where it is stored in compressed form. There are certain advantages of performing the resizing operation directly in the compressed domain. First, it saves the computational overhead of inverse and forward transforms. Next, by exploiting various properties of the transform domain, it is possible to design efficient fast algorithms providing good quality reconstructed image in the spatial domain. In this paper, we review a few algorithms for resizing an image by arbitrary sizes and provide a brief comparison of their performances.
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
在压缩域中调整图像大小
图像大小调整用于将给定大小的图像转换为不同大小的图像。存在不同的算法来调整图像的空间域,以及在频率域,它是存储在压缩形式。在压缩域中直接执行大小调整操作有一定的优点。首先,它节省了逆变换和正变换的计算开销。其次,利用变换域的各种特性,可以设计出在空间域中提供高质量重建图像的高效快速算法。在本文中,我们回顾了几种任意大小调整图像大小的算法,并简要比较了它们的性能。
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
Extended DES algorithm to Galois Fields Internet of Things and LoRa™ Low-Power Wide-Area Networks: A survey An adaptive solution for nonlinear system identification DMHT-LEACH: Dynamic multi-hop technique for wireless sensor networks Hybrid image interpolation technique based on nonlinear second and fourth-order diffusions
×
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