One attempt of a compression algorithm using the BWT

In 1994 Burrows and Wheeler [5] described a universal data compression algorithm (BW-algorithm, for short) which achieved compression rates that were close to the best known compression rates. Due to it’s simplicity, the algorithm can be implemented with relatively low complexity. Fenwick [8] described ideas to improve the efficiency (i.e. the compression rate) and complexity of the BW-algorithm. He also discusses relationships of the algorithm with other compression methods. Schindler [14] proposed a Burrows and Wheeler Transformation (BWT, for short) that is based on a limited ordering. This speeds up the algorithm for compression, but slows it down for decompression and slightly decreases the efficiency. Larsson [10] describes the relationship of the BWT with suffix and context trees. Sadakane [13] suggests a method to compute the BWT faster, and compares it to other methods. Recently Balkenhol and Kurtz [2] gave a thorough analysis of the BWT from an information theoretic point of view. They described implementation techniques for data compression algorithms based on the BWT, and developed a program with a better compression rate. In [4] these previous results on the BW-algorithm are improved. Based on the context tree model, the authors consider the specific statistical properties of the data at the output of the BWT. They describe six important properties. These considerations lead to modifications of the coding method, which in turn improve the coding efficiency. Further improvements related to the sorting are presented in [3, 11, 12].