DICTIONARY- BASED TEXT COMPRESSION TECHNIQUE USING QUATERNARY CODE

Abstract

Improving encoding and decoding time in compression technique is a great demand to modern users. In bit level compression technique, it requires more time to encode or decode every single bit when a binary code is used. The existing Huffman based algorithms use binary code which slow the decoding speed. This research proposes a new compression algorithm that makes use of a variation of the classic Huffman coding: quaternary Huffman coding. Using quaternary Huffman coding, each symbol is encoded into a quaternary code stream, instead of a binary bit stream. A quaternary code stream for Huffman coding requires a shorter Huffman tree, i.e., less depth. The potential benefit of a shorter Huffman tree is less traverse time, which improves both compression and decompression throughput. In this research, we analyze the properties of quaternary Huffman tree and conclude that a quaternary Huffman tree is usually one-third of the height from a binary tree. In this research, we develop a dictionary-based compression technique where we use a quaternary tree instead of a binary tree for construction of Human codes. Firstly, we explore the properties of quaternary tree structure mathematically for construction of Human codes. We study the terminology of new tree structure thoroughly and prove the results. Secondly, after a statistical analysis of English language; we design a variable length dictionary based on quaternary codes. Thirdly, we develop the encoding and decoding algorithms for the proposed technique. We compare the performance of the proposed technique with the existing popular techniques. The proposed technique performs better than the existing techniques with respect to decompression speed while the space requirement increases insignificantly.