Dictionary selection using partial matching

Abstract

This work concerns the search for text compressors that compress better than existing dictionary coders, but run faster than statistical coders. We describe a new method for text compression using multiple dictionaries, one for each context of preceding characters, where the contexts have varying lengths. The context to be used is determined using an escape mechanism similar to that of prediction by partial matching (PPM) methods. We describe modifications of three popular dictionary coders along these lines and experiments evaluating their effectiveness using the text files in the Calgary corpus. Our results suggest that modifying LZ77, LZFG, and LZW along these lines yields improvements in compression of about 3%, 6%, and 15%, respectively.