{"title":"参数源模型熵编码及其在图像和视频数据快速高效压缩中的应用","authors":"K. Minoo, Truong Q. Nguyen","doi":"10.1109/DCC.2009.80","DOIUrl":null,"url":null,"abstract":"In this paper a framework is proposed for efficient entropy coding of data which can be represented by a parametric distribution model. Based on the proposed framework, an entropy coder achieves coding efficiency by estimating the parameters of the statistical model (for the coded data), either via Maximum A Posteriori (MAP) or Maximum Likelihood (ML) parameter estimation techniques.","PeriodicalId":377880,"journal":{"name":"2009 Data Compression Conference","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Entropy Coding via Parametric Source Model with Applications in Fast and Efficient Compression of Image and Video Data\",\"authors\":\"K. Minoo, Truong Q. Nguyen\",\"doi\":\"10.1109/DCC.2009.80\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper a framework is proposed for efficient entropy coding of data which can be represented by a parametric distribution model. Based on the proposed framework, an entropy coder achieves coding efficiency by estimating the parameters of the statistical model (for the coded data), either via Maximum A Posteriori (MAP) or Maximum Likelihood (ML) parameter estimation techniques.\",\"PeriodicalId\":377880,\"journal\":{\"name\":\"2009 Data Compression Conference\",\"volume\":\"65 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-03-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Data Compression Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DCC.2009.80\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Data Compression Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DCC.2009.80","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Entropy Coding via Parametric Source Model with Applications in Fast and Efficient Compression of Image and Video Data
In this paper a framework is proposed for efficient entropy coding of data which can be represented by a parametric distribution model. Based on the proposed framework, an entropy coder achieves coding efficiency by estimating the parameters of the statistical model (for the coded data), either via Maximum A Posteriori (MAP) or Maximum Likelihood (ML) parameter estimation techniques.
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