A Comparison of JPEG and Wavelet Compression Applied to CT Images

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) imag...

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Main Authors: Saffor, Amhamed, Ramli, Abdul Rahman, Ng, Kwan Hoong
Format: Article
Language:English
English
Published: Universiti Putra Malaysia Press 2003
Online Access:http://psasir.upm.edu.my/id/eprint/3717/1/A_Comparison_of_JPEG_and_Wavelet_Compression.pdf
http://psasir.upm.edu.my/id/eprint/3717/
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Institution: Universiti Putra Malaysia
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spelling my.upm.eprints.37172013-05-27T07:10:40Z http://psasir.upm.edu.my/id/eprint/3717/ A Comparison of JPEG and Wavelet Compression Applied to CT Images Saffor, Amhamed Ramli, Abdul Rahman Ng, Kwan Hoong 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. Universiti Putra Malaysia Press 2003 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/3717/1/A_Comparison_of_JPEG_and_Wavelet_Compression.pdf Saffor, Amhamed and Ramli, Abdul Rahman and Ng, Kwan Hoong (2003) A Comparison of JPEG and Wavelet Compression Applied to CT Images. Pertanika Journal of Science & Technology, 11 (2). pp. 191-203. ISSN 0128-7680 English
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
English
description 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.
format Article
author Saffor, Amhamed
Ramli, Abdul Rahman
Ng, Kwan Hoong
spellingShingle Saffor, Amhamed
Ramli, Abdul Rahman
Ng, Kwan Hoong
A Comparison of JPEG and Wavelet Compression Applied to CT Images
author_facet Saffor, Amhamed
Ramli, Abdul Rahman
Ng, Kwan Hoong
author_sort Saffor, Amhamed
title A Comparison of JPEG and Wavelet Compression Applied to CT Images
title_short A Comparison of JPEG and Wavelet Compression Applied to CT Images
title_full A Comparison of JPEG and Wavelet Compression Applied to CT Images
title_fullStr A Comparison of JPEG and Wavelet Compression Applied to CT Images
title_full_unstemmed A Comparison of JPEG and Wavelet Compression Applied to CT Images
title_sort comparison of jpeg and wavelet compression applied to ct images
publisher Universiti Putra Malaysia Press
publishDate 2003
url http://psasir.upm.edu.my/id/eprint/3717/1/A_Comparison_of_JPEG_and_Wavelet_Compression.pdf
http://psasir.upm.edu.my/id/eprint/3717/
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