DocumentCode
1401751
Title
On fixed-database universal data compression with limited memory
Author
Hershkovits, Yehuda ; Ziv, Jocob
Author_Institution
Dept. of Electr. Eng., Technion-Israel Inst. of Technol., Haifa, Israel
Volume
43
Issue
6
fYear
1997
fDate
11/1/1997 12:00:00 AM
Firstpage
1966
Lastpage
1976
Abstract
The amount of fixed side information required for lossless data compression is discussed. Nonasymptotic coding and converse theorems are derived for data-compression algorithms with fixed statistical side information (“training sequence”) that is not large enough so as to yield the ultimate compression, namely, the entropy of the source
Keywords
sequences; source coding; converse theorems; entropy; fixed side information; fixed-database universal data compression; limited memory; lossless data compression; nonasymptotic coding; statistical side information; training sequence; Convergence; Data compression; Databases; Encoding; Entropy; Information theory; Jacobian matrices; Random variables; Source coding; Statistics;
fLanguage
English
Journal_Title
Information Theory, IEEE Transactions on
Publisher
ieee
ISSN
0018-9448
Type
jour
DOI
10.1109/18.641559
Filename
641559
Link To Document