{"title":"信号重构与压缩中的勒让德多项式","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":"{\"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}","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}
Legendre polynomials in signal reconstruction and compression
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.