{"title":"Point-wise analysis of redundancy in SWLZ algorithm for φ-mixing sources","authors":"Ayush Jain, R. Bansal","doi":"10.1109/ITW.2015.7133103","DOIUrl":null,"url":null,"abstract":"In this paper, we bound the number of phrases of the sliding window Lempel-Ziv (SWLZ) algorithm using an upper bound on the expected number of phrases in the fixed database Lempel-Ziv (FDLZ) algorithm for a class of φ-mixing sources which includes Markov sources, unifilar sources and finite state sources as special cases, as developed by Yang and Kieffer [1]. We use this bound to obtain a point-wise upper bound on the redundancy rate of SWLZ algorithm to be 2H(log<sub>2</sub>log<sub>2</sub>n<sub>w</sub>/log<sub>2</sub>n<sub>w</sub>) + O(log<sub>2</sub>log<sub>2</sub>log<sub>2</sub>n<sub>w</sub>/log<sub>2</sub>n<sub>w</sub>). Here H is the entropy rate of the source and n<sub>w</sub> is the window size.","PeriodicalId":174797,"journal":{"name":"2015 IEEE Information Theory Workshop (ITW)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Information Theory Workshop (ITW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITW.2015.7133103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, we bound the number of phrases of the sliding window Lempel-Ziv (SWLZ) algorithm using an upper bound on the expected number of phrases in the fixed database Lempel-Ziv (FDLZ) algorithm for a class of φ-mixing sources which includes Markov sources, unifilar sources and finite state sources as special cases, as developed by Yang and Kieffer [1]. We use this bound to obtain a point-wise upper bound on the redundancy rate of SWLZ algorithm to be 2H(log2log2nw/log2nw) + O(log2log2log2nw/log2nw). Here H is the entropy rate of the source and nw is the window size.