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) 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

A comparative study of image compression between JPEG and wavelet

Image compression is fundamental to the efficient and cost-effective use of digital medical imaging technology and applications. Wavelet transform techniques currently provide the most promising approach to high-quality image compression, which is essential for teleradiology and Picture Archiving and Communication System (PACS). In this study wavelet compression was applied to compress and decompress a digitized chest x-ray image at various compression ratios. The Wavelet Compression Engine (standard edition 2.5) was used in this study. This was then compared with the formal compression standard “Joint Photographic Expert Group” JPEG, using JPEG Wizard (standard edition 1.3.7). Currently there is no standard set of criteria for the clinical acceptability of compression ratio. Thus, histogram analysis, maximum absolute error (MAE), mean square error (MSE), root mean square error (RMSE), signal to noise ratio (SNR), and peak signal to noise ratio (PSNR) were used as a set of criteria to determine the ‘acceptability’ of image compression. The wavelet algorithm was found to have generally lower average error matrices and higher peak signal to noise ratios. Wavelet methods have been shown to have no significant differences in diagnostic accuracy for compression ratios of up to 30:1. Visual comparison was also made between the original image and compressed image to ascertain if there is any significant image degradation. Using wavelet algorithm, a very high compression ratio of up to 600:1 was achieved