Title page for ETD etd-07032007-100117


Type of Document Master's Thesis
Author Iombo, Claudio Jose
URN etd-07032007-100117
Title Predictive Data Compression Using Adaptive Arithmetic Coding
Degree Master of Science in Electrical Engineering (M.S.E.E.)
Department Electrical & Computer Engineering
Advisory Committee
Advisor Name Title
Subhash Kak Committee Chair
Hsiao-Chun Wu Committee Member
Xue-Bin Liang Committee Member
Keywords
  • markov models
  • information theory
  • arithmetic coding
  • huffman coding
  • data compression
Date of Defense 2007-06-13
Availability unrestricted
Abstract
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 Vitter’s implicit ordering of nodes.
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