{"title":"Burrows-Wheeler变换和移动到前端编码和运行长度编码的组合用于无损音频编码","authors":"H. Elsayed","doi":"10.1109/ICCES.2014.7030985","DOIUrl":null,"url":null,"abstract":"This paper presents a lossless audio coding using Burrows-Wheeler Transform (BWT) and a combination of a Move-To-Front coding (MTF) and Run Length Encoding (RLE). Audio signals used are assumed to be of floating point values. The BWT is applied to this floating point values to get the transformed coefficients; and then these resulting coefficients are converted using the Move-to-Front coding to coefficients can be better compressed and then these resulting coefficients are compressed using a combination of the Run Length Encoding, and entropy coding. Two entropy coding are used which are Arithmetic and Huffman coding. Simulation results show that the proposed lossless audio coding method outperforms other lossless audio coding methods; using only Burrows-Wheeler Transform method, using combined Burrows-Wheeler Transform and Move-to-Front coding method, and using combined Burrows-Wheeler Transform and Run Length Encoding method.","PeriodicalId":339697,"journal":{"name":"2014 9th International Conference on Computer Engineering & Systems (ICCES)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Burrows-Wheeler Transform and combination of Move-to-Front coding and Run Length Encoding for lossless audio coding\",\"authors\":\"H. Elsayed\",\"doi\":\"10.1109/ICCES.2014.7030985\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a lossless audio coding using Burrows-Wheeler Transform (BWT) and a combination of a Move-To-Front coding (MTF) and Run Length Encoding (RLE). Audio signals used are assumed to be of floating point values. The BWT is applied to this floating point values to get the transformed coefficients; and then these resulting coefficients are converted using the Move-to-Front coding to coefficients can be better compressed and then these resulting coefficients are compressed using a combination of the Run Length Encoding, and entropy coding. Two entropy coding are used which are Arithmetic and Huffman coding. Simulation results show that the proposed lossless audio coding method outperforms other lossless audio coding methods; using only Burrows-Wheeler Transform method, using combined Burrows-Wheeler Transform and Move-to-Front coding method, and using combined Burrows-Wheeler Transform and Run Length Encoding method.\",\"PeriodicalId\":339697,\"journal\":{\"name\":\"2014 9th International Conference on Computer Engineering & Systems (ICCES)\",\"volume\":\"120 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 9th International Conference on Computer Engineering & Systems (ICCES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCES.2014.7030985\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 9th International Conference on Computer Engineering & Systems (ICCES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCES.2014.7030985","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Burrows-Wheeler Transform and combination of Move-to-Front coding and Run Length Encoding for lossless audio coding
This paper presents a lossless audio coding using Burrows-Wheeler Transform (BWT) and a combination of a Move-To-Front coding (MTF) and Run Length Encoding (RLE). Audio signals used are assumed to be of floating point values. The BWT is applied to this floating point values to get the transformed coefficients; and then these resulting coefficients are converted using the Move-to-Front coding to coefficients can be better compressed and then these resulting coefficients are compressed using a combination of the Run Length Encoding, and entropy coding. Two entropy coding are used which are Arithmetic and Huffman coding. Simulation results show that the proposed lossless audio coding method outperforms other lossless audio coding methods; using only Burrows-Wheeler Transform method, using combined Burrows-Wheeler Transform and Move-to-Front coding method, and using combined Burrows-Wheeler Transform and Run Length Encoding method.