  Compression algorithms reduce the redundancy in data representation to
decrease the storage required for that data. Data compression offers an
attractive approach to reducing communication costs by using available
bandwidth effectively. Over the last decade there has been an unprecedented
explosion in the amount of digital data transmitted via the Internet,
representing text, images, video, sound, computer programs, etc. With this
trend expected to continue, it makes sense to pursue research on developing
algorithms that can most effectively use available network bandwidth by
maximally compressing data. This research paper is focused on addressing this
problem of lossless compression of text files. Lossless compression researchers
have developed highly sophisticated approaches, such as Huffman encoding,
arithmetic encoding, the Lempel-Ziv family, Dynamic Markov Compression (DMC),
Prediction by Partial Matching (PPM), and Burrows-Wheeler Transform (BWT) based
algorithms. However, none of these methods has been able to reach the
theoretical best-case compression ratio consistently, which suggests that
better algorithms may be possible. One approach for trying to attain better
compression ratios is to develop new compression algorithms. An alternative
approach, however, is to develop intelligent, reversible transformations that
can be applied to a source text that improve an existing, or backend,
algorithm's ability to compress. The latter strategy has been explored here.
