A Novel Hybrid Medical Data Compression Using Huffman Coding and LZW in IoT

Mohammadi, Hossein; Ghaderzadeh, Abdulbaghi; Ahmadi, Amir Sheikh

doi:10.1080/03772063.2022.2052985

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…In another research, a compression method combines the information-rearranging Lempel-Ziv-Welch (LZW) algorithm and the Huffman encoding method, both of which are lossless compression techniques. Hoffman and LZW approaches are combined in this way that a binary information arrangement is employed, resulting in an information mapping that is fully unique for each piece of information while yet simplifying the mapping, making this article stand out (Mohammadi et al, 2022).…”

Section: Literature Surveymentioning

confidence: 99%

Quaternary Tree Structure as a Novel Method for Huffman Coding Tree

Habib,

Islam,

Rahman

2023

Journal of Computer Science

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The binary code used by the current Huffman-based techniques slows down decoding speed. The quaternary Huffman coding method is a fresh take on the traditional Huffman coding that is suggested in this study. Each symbol is encoded into a quaternary code rather than a binary code using the quaternary Huffman coding technique. For Huffman coding, a quaternary code stream necessitates a shallower Huffman tree. Less traverse time could be a benefit of a shorter Huffman tree, with the enhancement of both encoding and decoding speed. The main objectives of this research are to verify the feasibility of using quaternary code in terms of both decoding speed and memory usage mathematically, to develop a new code generation technique called quaternary Huffman coding, and to analyze the performance of the proposed system in terms of both storage and decoding time. In this study, we first quantitatively investigate the quaternary tree structure's features for building Huffman codes. We thoroughly investigate the new tree structure's nomenclature and substantiate the findings. It is found in the research that the quaternary code performs better than binary code in terms of decoding speed in data compression and decompression techniques.

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Section: Literature Surveymentioning

confidence: 99%