In this paper we present an approach which is an alternative to compression algorithms in vogue 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. This is aimed at developing generic, reversible transformations that can be applied to a source text that improve an existing, or backend, algorithm's ability to compress. In this connection, we present two lossless, reversible transformations namely Enhanced Intelligent Dictionary Based Encoding (EIDBE) and Improved Intelligent Dictionary Based Encoding (IIDBE). We then provide experimental results over the files chosen from the classical text corpus.
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