{"title":"采用双霍夫曼最小方差编码技术实现高效无损压缩","authors":"G. Sandeep, B. S. Sunil kumar, D. Deepak","doi":"10.1109/ICATCCT.2015.7456942","DOIUrl":null,"url":null,"abstract":"A Huffman code is a particular type of optimal prefix code that is commonly used for loss-less data compression. The process of finding such a code is known as Huffman coding. The output from Huffman's algorithm can be viewed as a variable-length code table for encoding a source symbol. The algorithm derives this table from the estimated probability or frequency of occurrence for each possible value of the source symbol. In this paper, we present a new approach to measure the performance and redundancy that work on two methods of coding like Huffman coding and Minimum Variance Huffman Coding. After getting the code-word for each symbol, we compress it on the basis of its binary values like 0 and 1 using binary coding. This is applied to both the approaches; this process is called as Double Huffman Coding. Finally we produce a better result than Huffman coding.","PeriodicalId":276158,"journal":{"name":"2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"An efficient lossless compression using double Huffman minimum variance encoding technique\",\"authors\":\"G. Sandeep, B. S. Sunil kumar, D. Deepak\",\"doi\":\"10.1109/ICATCCT.2015.7456942\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A Huffman code is a particular type of optimal prefix code that is commonly used for loss-less data compression. The process of finding such a code is known as Huffman coding. The output from Huffman's algorithm can be viewed as a variable-length code table for encoding a source symbol. The algorithm derives this table from the estimated probability or frequency of occurrence for each possible value of the source symbol. In this paper, we present a new approach to measure the performance and redundancy that work on two methods of coding like Huffman coding and Minimum Variance Huffman Coding. After getting the code-word for each symbol, we compress it on the basis of its binary values like 0 and 1 using binary coding. This is applied to both the approaches; this process is called as Double Huffman Coding. Finally we produce a better result than Huffman coding.\",\"PeriodicalId\":276158,\"journal\":{\"name\":\"2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICATCCT.2015.7456942\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICATCCT.2015.7456942","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An efficient lossless compression using double Huffman minimum variance encoding technique
A Huffman code is a particular type of optimal prefix code that is commonly used for loss-less data compression. The process of finding such a code is known as Huffman coding. The output from Huffman's algorithm can be viewed as a variable-length code table for encoding a source symbol. The algorithm derives this table from the estimated probability or frequency of occurrence for each possible value of the source symbol. In this paper, we present a new approach to measure the performance and redundancy that work on two methods of coding like Huffman coding and Minimum Variance Huffman Coding. After getting the code-word for each symbol, we compress it on the basis of its binary values like 0 and 1 using binary coding. This is applied to both the approaches; this process is called as Double Huffman Coding. Finally we produce a better result than Huffman coding.