IMPROVEMENTS OF LOSSLESS IMAGE COMPRESSION WITH KERNEL BASED GLOBAL STRUCTURE TRANSFORM.PDF

Abstract

Image compression addresses the problem of reducing the amount of data required to represent an image, whereas Lossless Image Compression refers to the application of the data compression in which the information of the original image is retained intact after compression hence reducing the storage space and transmission bandwidth. In our everyday life, images are processed, transmitted and stored digitally in various devices such as Digital cameras, iPods, medical images (Magnetic resonance imaging and Computer Tomographyscans) and digital telescopes. The Burrows-Wheeler Compression Algorithm (BWCA) is the type of block sorting transform which was first introduced for lossless data compression, at first BWCA was used for text compression, but rapidly applications of BWCA was developed in the field of lossless image compression, many improvements has been offered by number of researchers since the creation of BWCA in 1994. Other studies treat the entropy coding of the data stream. Finally, many publications concern the middle part of the algorithm, where the BWT output symbols are prepared for the following entropy coding. The Global Structure Transform (GST) is the stage in BWCA which transforms the local data in global context, such as in image compression the gray-levels can lay in variety of groups and bands, but the GST. Several researches have been conducted on techniques to improve the efficiency of BWCA; most of the studies focus on a specific stage improvement. We focus on enhancement of lossless image compression due to the aimed applications especially in the medical field; nevertheless this scheme can be applied for lossless image compression as well as for lossy image compression.