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.
