P. S. Srinivasa Rao, B. Srinivasa Rao, Santhosh Kumar, G. Sreenivasulu
{"title":"Analysis And Synthesis of Image Using Wavelet Transform and Compression Methods","authors":"P. S. Srinivasa Rao, B. Srinivasa Rao, Santhosh Kumar, G. Sreenivasulu","doi":"10.1109/i-PACT44901.2019.8960074","DOIUrl":null,"url":null,"abstract":"In this paper, deal with image for analyze and compress without loss of brightness by wavelet transformation with various compression methods. Generally, image or signal can be analysed either frequency or time domain. In wavelet transformation the image can be considered in frequency and time domain. In this technique image can be compressed or reduced about 70% of original image without loss of brightness. The wavelet transformation is performed for small and large data. It can be chunked signal from large signal and examined for small signal without maximum loss of threshold.","PeriodicalId":214890,"journal":{"name":"2019 Innovations in Power and Advanced Computing Technologies (i-PACT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Innovations in Power and Advanced Computing Technologies (i-PACT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/i-PACT44901.2019.8960074","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 this paper, deal with image for analyze and compress without loss of brightness by wavelet transformation with various compression methods. Generally, image or signal can be analysed either frequency or time domain. In wavelet transformation the image can be considered in frequency and time domain. In this technique image can be compressed or reduced about 70% of original image without loss of brightness. The wavelet transformation is performed for small and large data. It can be chunked signal from large signal and examined for small signal without maximum loss of threshold.