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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Universiti Putra Malaysia Press
2003
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| Online Access: | http://psasir.upm.edu.my/id/eprint/3717/1/A_Comparison_of_JPEG_and_Wavelet_Compression.pdf |
| _version_ | 1796966606298939392 |
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| author | Saffor, Amhamed Ramli, Abdul Rahman Ng, Kwan Hoong |
| author_facet | Saffor, Amhamed Ramli, Abdul Rahman Ng, Kwan Hoong |
| author_sort | Saffor, Amhamed |
| collection | UPM |
| 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. |
| first_indexed | 2024-03-06T07:01:56Z |
| format | Article |
| id | upm.eprints-3717 |
| institution | Universiti Putra Malaysia |
| language | English English |
| last_indexed | 2024-03-06T07:01:56Z |
| publishDate | 2003 |
| publisher | Universiti Putra Malaysia Press |
| record_format | dspace |
| spelling | 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 |
| spellingShingle | Saffor, Amhamed Ramli, Abdul Rahman Ng, Kwan Hoong A Comparison of JPEG and Wavelet Compression Applied to CT Images |
| title | 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_short | A Comparison of JPEG and Wavelet Compression
Applied to CT Images |
| title_sort | comparison of jpeg and wavelet compression applied to ct images |
| url | http://psasir.upm.edu.my/id/eprint/3717/1/A_Comparison_of_JPEG_and_Wavelet_Compression.pdf |
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