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Enhancing adaptive huffman coding through word by word compression for textual data

Publication Type : Conference Paper

Publisher : 2017 International Conference on Communication and Signal Processing (ICCSP)

Source : 2017 International Conference on Communication and Signal Processing (ICCSP), IEEE, Chennai, India (2017)

Url : https://ieeexplore.ieee.org/abstract/document/8286534

Keywords : adaptive Huffman coding, adaptive Huffman encoding technique, Algorithm design and analysis, Channel coding, compression ratio, computational efforts, Data compression, data compression algorithms, Decoding, Dictionaries, dictionary, Huffman codes, Huffman coding, huffman tree, Image coding, intermediate file, text analysis, text compression, text file compression, textual data, word by word, word compression

Campus : Amritapuri

School : Department of Computer Science and Engineering, School of Engineering

Center : Center for Computational Engineering and Networking

Department : Center for Computational Engineering and Networking (CEN), Computer Science

Verified : Yes

Year : 2017

Abstract : Nowadays, the volume of information that is being processed is increasing exponentially. And hence, the significance of data compression algorithms is also increasing. Data compression algorithms aim at reducing the size of data at the cost of increased computational efforts. In this paper, we propose an enhanced version of adaptive Huffman encoding technique for improving the efficiency of text file compression. In the proposed encoding technique, we devise a mechanism for compressing the text files word by word rather than the existing byte by byte compression technique. The proposed method is implemented and the results showed better performance in terms of compression ratio as well as execution time.

Cite this Research Publication : D. Dath and Vinitha Panicker J, “Enhancing adaptive huffman coding through word by word compression for textual data”, in 2017 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 2017

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