02-05-2011, 11:56 AM
Intelligent Dictionary Based Encoding Algorithm for Text Data Compression for High-Speed Data Transmission over Internet (Java)
Proceedings on Data Compression,
Abstract:
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. Michael Burrows and David Wheeler recently released the details of a transformation function that opens the door to some revolutionary new data compression techniques. The Burrows-Wheeler Transform, or BWT, transforms a block of data into a format that is extremely well suited for compression. The block sorting algorithm they developed works by applying a reversible transformation to a block of input text. The transformation does not itself compress the data, but reorders it to make it easy to compress with simple algorithms such as move to front encoding. The basic philosophy adopted by us in this paper is to preprocess the text and transform it into some intermediate form which can be compressed with better efficiency and which exploits the natural redundancy of the language in making the transformation. A strategy called Intelligent Dictionary Based Encoding (IDBE) is discussed to achieve this. It has been observed that a preprocessing of the text prior to conventional compression will improve the compression efficiency much better. The intelligent dictionary based encryption provides the required security.
Proceedings on Data Compression,
Abstract:
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. Michael Burrows and David Wheeler recently released the details of a transformation function that opens the door to some revolutionary new data compression techniques. The Burrows-Wheeler Transform, or BWT, transforms a block of data into a format that is extremely well suited for compression. The block sorting algorithm they developed works by applying a reversible transformation to a block of input text. The transformation does not itself compress the data, but reorders it to make it easy to compress with simple algorithms such as move to front encoding. The basic philosophy adopted by us in this paper is to preprocess the text and transform it into some intermediate form which can be compressed with better efficiency and which exploits the natural redundancy of the language in making the transformation. A strategy called Intelligent Dictionary Based Encoding (IDBE) is discussed to achieve this. It has been observed that a preprocessing of the text prior to conventional compression will improve the compression efficiency much better. The intelligent dictionary based encryption provides the required security.