With the rapid growing of data and number of applications, there is a crucial need of dictionary based reversible transformation techniques to increase the efficiency of the compression algorithms and hence contribute towards the enhancement in compression ratio. Performance analysis of compression methods in combination with the various transformation techniques is obtained for different text files of varying sizes. The popular block sorting lossless Burrows Wheeler Compression Algorithm (BWCA) is implemented along with one proposed method. For efficient compression a dictionary based transformation algorithm is also developed. It is observed that much increase in terms of compression ratio is attained when a source file is preprocessed with dictionary and then applied to BWCA and the proposed method.
This paper presents a new text transformation method, which has a few similarities with the StarNT text transformation method. StarNT is a dictionarybased lossless text transform algorithm. Many different compression methods have been devised by researchers to find a suitable solution of data transmission that utilises the entire network bandwidth optimally and which also achieves a higher compression ratio. Most of the approaches that are being used, like the Prediction by Partial Matching (PPM), Burrows-Wheeler Transform have been unable to achieve the best possible output as provided by theoretical calculations and hence have left researchers to find more efficient techniques of text compression. Further in this paper we also provide the experimental results of the timing performance and space utilisation by compression of our algorithm by comparing with StarNT method.
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