A study of image compression is becoming more important since an
uncompressed image requires a large amount of storage space and high
transmission bandwidth. This paper focuses on the quantitative comparison of
lossy compression methods applied to a variety of 8-bit Computed Tomography
(CT) images. Joint Photographic Experts Group UPEG) and Wavelet
compression algorithms were used on a set of CT images, namely brain, chest,
and abdomen. These algorithms were applied to each image to achieve
maximum compression ratio (CR). Each compressed image was then decompressed and quantitative analysis was performed to compare each
compressed-then-decompressed image with its corresponding original image.
The Wavelet Compression Engine (standard edition 2.5), and ]pEG Wizard
(Version 1.1.7) were used in this study. The statistical indices computed were
mean square error (MSE) , signal-to-noise ratio (SNR) and peak signal-to-noise
ratio (PSNR). Our results show that Wavelet compression yields better
compression quality compared with ]pEG for higher compression. From the
numerical values obtained we observe that the PSNR for chest and abdomen
images is equal to 24 dB for compression ratio up to 31:1 by using ]pEG and
18 dB for compression ratio up to 33:1 by using wavelet. For brain image the
PSNR is equal to 26 to 30 dB for compression ratio between 40 to 125:1 by
using ]pEG, whereas by using wavelet the PSNR is equal to 22 to 34 dB for
compression ratio between 52 to 240:1. The degree of compression was also
found dependent on the anatomic structure and the complexity of the CT
images
