Variable-length contexts for PPM

Abstract

This paper presents a PPM variation which combines traditional character based processing with string matching. Such an approach can effectively handle repetitive data and can be used with practically any algorithm from the PPM family. The algorithm, inspired by its predecessors, PPM/sup */ and PPMZ, searches for matching sequences in arbitrarily long, variable-length, deterministic contexts. The experimental results show that the proposed technique may be very useful, especially in combination with relatively low order (up to 8) models, where the compression gains are often significant and the additional memory requirements are moderate.