Title :
On sliding-window universal data compression with limited memory
Author :
Hershkovits, Yehuda ; Ziv, Jacob
Author_Institution :
Adv. Recog. Technols., Tel Aviv, Israel
fDate :
1/1/1998 12:00:00 AM
Abstract :
Nonasymptotic coding and converse theorems are derived for universal data compression algorithms in cases where the training sequence (“history”) that is available to the encoder consists of the most recent segment of the input data string that has been processed, but is not large enough so as to yield the ultimate compression, namely, the entropy of the source
Keywords :
data compression; sequences; source coding; converse theorems; encoder; history; input data string; limited memory; nonasymptotic coding theorem; sliding-window universal data compression; source coding; source entropy; training sequence; universal data compression algorithms; Convergence; Data compression; Entropy; Information theory; Jacobian matrices; Random variables; Source coding; Statistics;
Journal_Title :
Information Theory, IEEE Transactions on
