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Efficient Adaptive Data Compression Using Fano Binary Search Trees

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Computer and Information Sciences - ISCIS 2005 (ISCIS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3733))

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Abstract

In this paper, we show an effective way of using adaptive self-organizing data structures in enhancing compression schemes. We introduce a new data structure, the Partitioning Binary Search Tree (PBST), which is based on the well-known Binary Search Tree (BST), and when used in conjunction with Fano encoding, the PBST leads to the so-called Fano Binary Search Tree (FBST). The PBST and FBST can be maintained adaptively and in a self-organizing manner by using new tree-based operators, namely the Shift-To-Left (STL) and the Shift-To-Right (STR) operators. The encoding and decoding procedures that also update the FBST have been implemented, and show that the adaptive Fano coding using FBSTs, the Huffman, and the greedy adaptive Fano coding achieve similar compression ratios.

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Rueda, L., Oommen, B.J. (2005). Efficient Adaptive Data Compression Using Fano Binary Search Trees. In: Yolum, p., Güngör, T., Gürgen, F., Özturan, C. (eds) Computer and Information Sciences - ISCIS 2005. ISCIS 2005. Lecture Notes in Computer Science, vol 3733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569596_79

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  • DOI: https://doi.org/10.1007/11569596_79

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29414-6

  • Online ISBN: 978-3-540-32085-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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