{"title":"Legendre polynomials in signal reconstruction and compression","authors":"Guoqi Li, C. Wen","doi":"10.1109/ICIEA.2010.5514776","DOIUrl":null,"url":null,"abstract":"In this paper, we present a method for signal reconstruction using orthogonal transform based on discrete Legendre polynomials. Using such a transform provides computational advantages over polynomial basis. We extend the discrete Legendre polynomials to two-dimensional discrete Legendre polynomials for reconstructing and compressing an image. In the applications, we notice that when the order of a polynomial becomes large, the proposed method tends to exhibit numerical instabilities. We bring forward a possible way to avoid such instabilities. Simulation results illustrate that the error resulted from compression is usually low with a satisfactory compression ratio by using the proposed method. An application in system identification is also presented.","PeriodicalId":234296,"journal":{"name":"2010 5th IEEE Conference on Industrial Electronics and Applications","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 5th IEEE Conference on Industrial Electronics and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIEA.2010.5514776","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
In this paper, we present a method for signal reconstruction using orthogonal transform based on discrete Legendre polynomials. Using such a transform provides computational advantages over polynomial basis. We extend the discrete Legendre polynomials to two-dimensional discrete Legendre polynomials for reconstructing and compressing an image. In the applications, we notice that when the order of a polynomial becomes large, the proposed method tends to exhibit numerical instabilities. We bring forward a possible way to avoid such instabilities. Simulation results illustrate that the error resulted from compression is usually low with a satisfactory compression ratio by using the proposed method. An application in system identification is also presented.