A Comparison of JPEG and Wavelet Compression Applied to Computed Tomography Brain, Chest, and Abdomen Images
Amhmed Saffor, Kwan Hoong Ng, Abdul Rahman Ramli, David J. Dowsett · The Internet Journal of Radiology · 2001
Background: 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. Method: Joint Photographic Experts Group (JPEG) 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-thendecompressed image with its corresponding original image. The Wavelet Compression Engine (standard edition 2.5), and JPEG 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). Results: Our results mostly agreed with other published studies, which show that Wavelet compression yields better compression quality at constant compressed file sizes compared with JPEG. Conclusion: The degree of compression is dependent on anatomic structures and complexity of diagnostic information in the image so careful consideration must be given to the level of compression ratio before archiving clinical images otherwise essential information will be lost.