小波变换与离散小波变换在彩色图像压缩中的比较

Pub Date : 2020-01-20 DOI:10.31289/jite.v3i2.3154
Christnatalis Christnatalis, B. Bachtiar, Rony Rony
{"title":"小波变换与离散小波变换在彩色图像压缩中的比较","authors":"Christnatalis Christnatalis, B. Bachtiar, Rony Rony","doi":"10.31289/jite.v3i2.3154","DOIUrl":null,"url":null,"abstract":"In this research, the algorithm used to compress images is using the haar wavelet transformation method and the discrete wavelet transform algorithm. The image compression based on Wavelet Wavelet transform uses a calculation system with decomposition with row direction and decomposition with column direction. While discrete wavelet transform-based image compression, the size of the compressed image produced will be more optimal because some information that is not so useful, not so felt, and not so seen by humans will be eliminated so that humans still assume that the data can still be used even though it is compressed. The data used are data taken directly, so the test results are obtained that digital image compression based on Wavelet Wavelet Transformation gets a compression ratio of 41%, while the discrete wavelet transform reaches 29.5%. Based on research problems regarding the efficiency of storage media, it can be concluded that the right algorithm to choose is the Haar Wavelet transformation algorithm. To improve compression results it is recommended to use wavelet transforms other than haar, such as daubechies, symlets, and so on.","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparative Compression of Wavelet Haar Transformation with Discrete Wavelet Transform on Colored Image Compression\",\"authors\":\"Christnatalis Christnatalis, B. Bachtiar, Rony Rony\",\"doi\":\"10.31289/jite.v3i2.3154\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this research, the algorithm used to compress images is using the haar wavelet transformation method and the discrete wavelet transform algorithm. The image compression based on Wavelet Wavelet transform uses a calculation system with decomposition with row direction and decomposition with column direction. While discrete wavelet transform-based image compression, the size of the compressed image produced will be more optimal because some information that is not so useful, not so felt, and not so seen by humans will be eliminated so that humans still assume that the data can still be used even though it is compressed. The data used are data taken directly, so the test results are obtained that digital image compression based on Wavelet Wavelet Transformation gets a compression ratio of 41%, while the discrete wavelet transform reaches 29.5%. Based on research problems regarding the efficiency of storage media, it can be concluded that the right algorithm to choose is the Haar Wavelet transformation algorithm. To improve compression results it is recommended to use wavelet transforms other than haar, such as daubechies, symlets, and so on.\",\"PeriodicalId\":0,\"journal\":{\"name\":\"\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0,\"publicationDate\":\"2020-01-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31289/jite.v3i2.3154\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31289/jite.v3i2.3154","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

在本研究中,所使用的图像压缩算法分别是haar小波变换方法和离散小波变换算法。基于小波变换的图像压缩采用了行方向分解和列方向分解的计算系统。而基于离散小波变换的图像压缩,产生的压缩图像的大小将是更理想的,因为一些不那么有用的信息,不那么感觉,不那么被人类看到的信息将被消除,这样人类仍然认为数据仍然可以使用,即使它被压缩了。所使用的数据均为直接采集的数据,因此测试结果表明,基于小波变换的数字图像压缩压缩比为41%,而离散小波变换压缩比为29.5%。通过对存储介质效率问题的研究,得出Haar小波变换算法是合适的算法选择。为了改善压缩效果,建议使用小波变换而不是haar变换,如daubechies、symlets等。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
分享
查看原文
Comparative Compression of Wavelet Haar Transformation with Discrete Wavelet Transform on Colored Image Compression
In this research, the algorithm used to compress images is using the haar wavelet transformation method and the discrete wavelet transform algorithm. The image compression based on Wavelet Wavelet transform uses a calculation system with decomposition with row direction and decomposition with column direction. While discrete wavelet transform-based image compression, the size of the compressed image produced will be more optimal because some information that is not so useful, not so felt, and not so seen by humans will be eliminated so that humans still assume that the data can still be used even though it is compressed. The data used are data taken directly, so the test results are obtained that digital image compression based on Wavelet Wavelet Transformation gets a compression ratio of 41%, while the discrete wavelet transform reaches 29.5%. Based on research problems regarding the efficiency of storage media, it can be concluded that the right algorithm to choose is the Haar Wavelet transformation algorithm. To improve compression results it is recommended to use wavelet transforms other than haar, such as daubechies, symlets, and so on.
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
×
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