Empirical context allocation for multiple dictionary data compression

Abstract

A class of multiple dictionary Lempel-Ziv algorithms is described, where a set of context dependent dictionaries are maintained, and a dictionary chosen based on empirical performance data. These algorithms are conceptually simpler than an earlier approach based on dynamic programming[1] and are also asymptotically optimal.