Smart Multimedia Compressor—Intelligent Algorithms for Text and Image Compression

Abstract

Abstract A number of text compression algorithms have been proposed in the past decades, which have been very effective and usually operate on conventional character/word-based approaches. A novel data compression perspective of data mining is explored in this research and the paper focuses on novel frequent sequence/pattern mining approach to text compression. This work attempts to make use of longer-range correlations between words in languages for achieving better text compression. We propose a novel and efficient method by making the compression of any word-level text in a universal manner for corpora across domains referred as Universal Huffman Tree-based encoding. The major contribution of this work is in terms of avoidance of code table communication to the decoder. Simulation results over benchmark datasets indicate that Universal Huffman encoding employing frequent sequence mining (achieves [20%, 89%] improvement in compression in reduced time. The paper also contributes a usable interface for Data compression that employs the proposed frequent sequent mining-based data compression algorithm. The interface supports features such as feedback, consistency, usability, navigation, visual appeal, performance and accessibility on par with existing compression softwares. The work results in an intelligent data compression software employing knowledge engineering perspective.