Incompressibility and Lossless Data Compression: An Approach by Pattern Discovery

Abstract

Abstract. We present a novel method for lossless data compression that aims to get a different performance to those proposed in the last decades to tackle the underlying volume of data of the Information and Multimedia Ages. These latter methods are called entropic or classic because they are based on the Classic Information Theory of Claude E. Shannon and include Huffman [8], Arithmetic [14], Lempel-Ziv [15], Burrows Wheeler (BWT) [4], Move To Front (MTF) [3] and Prediction by Partial Matching (PPM) [5] techniques. We review the Incompressibility Theorem and its relation with classic methods and our method based on discovering symbol patterns called metasymbols. Experimental results allow us to propose metasymbolic compression as a tool for multimedia compression, sequence analysis and unsupervised clustering.