Improving PPM Algorithm Using Dictionaries

TL;DR: In this paper, a character-based PPM text compression algorithm for natural languages is proposed, in which nonwords and prefixes of words are encoded using characterbased context models and suffix of words using dictionary models.

Abstract: We propose a method to improve traditional character-based PPM text compression algorithm [1] for natural languages. Consider a text file as a sequence of alternating words and non-words, the basic idea of our algorithm is to encode nonwords and prefixes of words using character-based context models and encode suffixes of words using dictionary models. By using dictionary models, the algorithm can encode multiple characters as a whole, and thus enhance the compression efficiency. The advantages of the proposed algorithm are: 1) it does not require any text preprocessing, 2) it does not need any explicit codeword to identify switch between context and dictionary models, 3) it can be applied to any character-based PPM algorithms without incurring much additional computational cost.