Abstract
Data Compression has been one of the enabling technologies for the on-going digital multimedia revolution for decades which resulted in renowned algorithms like Huffman Encoding, LZ77, Gzip, RLE and JPEG etc. Researchers have looked into the character/word based approaches to Text and Image Compression missing out the larger aspect of pattern mining from large databases. The central theme of our compression research focuses on the Compression perspective of Data Mining as suggested by Naren Ramakrishnan et al. wherein efficient versions of seminal algorithms of Text/Image compression are developed using various Frequent Pattern Mining(FPM)/Clustering techniques. This paper proposes a cluster of novel and hybrid efficient text and image compression algorithms employing efficient data structures like Hash and Graphs. We have retrieved optimal set of patterns through pruning which is efficient in terms of database scan/storage space by reducing the code table size. Moreover, a detailed analysis of time and space complexity is performed for some of our approaches and various text structures are proposed. Simulation results over various spare/dense benchmark text corpora indicate 18% to 751% improvement in compression ratio over other state of the art techniques. In Image compression, our results showed up to 45% improvement in compression ratio and up to 40% in image quality efficiency.