Predictive Data Compression Using Adaptive Arithmetic Coding
The commonly used data compression techniques do not necessarily provide maximal compression and neither do they define the most efficient framework for transmission of data. In this thesis we investigate variants of the standard compression algorithms that use the strategy of partitioning of the da...
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ndltd-LSU-oai-etd.lsu.edu-etd-07032007-1001172013-01-07T22:51:19Z Predictive Data Compression Using Adaptive Arithmetic Coding Iombo, Claudio Jose Electrical & Computer Engineering The commonly used data compression techniques do not necessarily provide maximal compression and neither do they define the most efficient framework for transmission of data. In this thesis we investigate variants of the standard compression algorithms that use the strategy of partitioning of the data to be compressed. Doing so not only increases the compression ratio in many instances, it also reduces the maximum data block size for transmission. The partitioning of the data is made using a Markov model to predict if doing so would result in increased compression ratio. Experiments have been performed on text files comparing the new scheme to adaptive Huffman and arithmetic coding methods. The adaptive Huffman method has been implemented in a new way by combining the FGK method with Vitters implicit ordering of nodes. Subhash Kak Hsiao-Chun Wu Xue-Bin Liang LSU 2007-07-11 text application/pdf http://etd.lsu.edu/docs/available/etd-07032007-100117/ http://etd.lsu.edu/docs/available/etd-07032007-100117/ en unrestricted I hereby certify that, if appropriate, I have obtained and attached herein a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to LSU or its agents the non-exclusive license to archive and make accessible, under the conditions specified below and in appropriate University policies, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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Electrical & Computer Engineering |
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Electrical & Computer Engineering Iombo, Claudio Jose Predictive Data Compression Using Adaptive Arithmetic Coding |
description |
The commonly used data compression techniques do not necessarily provide maximal compression and neither do they define the most efficient framework for transmission of data. In this thesis we investigate variants of the standard compression algorithms that use the strategy of partitioning of the data to be compressed. Doing so not only increases the compression ratio in many instances, it also reduces the maximum data block size for transmission. The partitioning of the data is made using a Markov model to predict if doing so would result in increased compression ratio. Experiments have been performed on text files comparing the new scheme to adaptive Huffman and arithmetic coding methods. The adaptive Huffman method has been implemented in a new way by combining the FGK method with Vitters implicit ordering of nodes. |
author2 |
Subhash Kak |
author_facet |
Subhash Kak Iombo, Claudio Jose |
author |
Iombo, Claudio Jose |
author_sort |
Iombo, Claudio Jose |
title |
Predictive Data Compression Using Adaptive Arithmetic Coding |
title_short |
Predictive Data Compression Using Adaptive Arithmetic Coding |
title_full |
Predictive Data Compression Using Adaptive Arithmetic Coding |
title_fullStr |
Predictive Data Compression Using Adaptive Arithmetic Coding |
title_full_unstemmed |
Predictive Data Compression Using Adaptive Arithmetic Coding |
title_sort |
predictive data compression using adaptive arithmetic coding |
publisher |
LSU |
publishDate |
2007 |
url |
http://etd.lsu.edu/docs/available/etd-07032007-100117/ |
work_keys_str_mv |
AT iomboclaudiojose predictivedatacompressionusingadaptivearithmeticcoding |
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