{"title":"A Lagrangian formulation of fixed-rate quantization","authors":"R. Gray","doi":"10.1109/DCC.2005.7","DOIUrl":null,"url":null,"abstract":"A Lagrangian formulation of fixed-rate vector quantization is presented. The formulation provides an alternative version of the classic high-rate quantization approximations for fixed-rate codes of Zador (1966), and Bucklew and Wise (1982) which parallels the Lagrangian results for variable-rate codes and it leads to a variation of the classic Lloyd (1982) algorithm for quantizer design. The approach also leads to a natural Lagrangian formulation combining both common rate constraints of alphabet size and entropy, effectively providing a Lagrangian formulation of memory and entropy constrained vector quantization.","PeriodicalId":91161,"journal":{"name":"Proceedings. Data Compression Conference","volume":"176 5 1","pages":"261-269"},"PeriodicalIF":0.0000,"publicationDate":"2005-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/DCC.2005.7","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. Data Compression Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DCC.2005.7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
A Lagrangian formulation of fixed-rate vector quantization is presented. The formulation provides an alternative version of the classic high-rate quantization approximations for fixed-rate codes of Zador (1966), and Bucklew and Wise (1982) which parallels the Lagrangian results for variable-rate codes and it leads to a variation of the classic Lloyd (1982) algorithm for quantizer design. The approach also leads to a natural Lagrangian formulation combining both common rate constraints of alphabet size and entropy, effectively providing a Lagrangian formulation of memory and entropy constrained vector quantization.