Comparative Analysis of Lempel-Ziv-Welch and Embedded Zero Tree Compression Algorithms on Kidney Images.
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Date
2020-07-24
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Published by Faculty of Computing and Information Science. University of Ilorin. Special Issue on Computing and Communication Technologies
Abstract
Image compression refers to the reduction of digital image data to store or transmit images with less rate of
distortion. It is the reduction of the quantity of graphical data required to represent a digital image. Medical
images play an important role in providing detailed information about patient injuries, fractures and other
critical issues related to different disease and discomfort. Transmission of a reduced size of image data will help
reduce computational burden as well as storage cost. Several compression algorithms have been adopted for use
by numerous research on different types images, of which embedded zero tree (EZT) and Lempel-Ziv-Wetch
(LZW) compression algorithms are most commonly used amongst others. The choice of a suitable compression
algorithm for compression task on specific images has been an open issue, which this research seeks to look into.
In this research, the compression capability of embedded zero tree (EZT) and Lempel-Ziv-Wetch (LZW)
compression algorithms on Kidney images was compared. The performance of these algorithms was evaluated
using peak signal noise ratio (PSNR), and compression ratio (CR). Effective compression implies that the
resultant stream of image will be smaller than the original image size, without loss of vital content. Empirical
results obtained shows that the LZW, having PSNR and CR of 97.28 and 2.85 respectively outperforms EZT with
PSNR of 89.17 and CR of 1.82 when the same sets of images were subjected under the two compression
algorithms. The results from this research suggest the usability of LZW for compression task involving kidney
images. Similarly, lossless compression is recommended because loss of vital information content of medical
images could lead to wrong diagnosis.
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Babatunde et. al., 2020