Applying wavelet transforms with arithmetic coding to radiological image compression

Radiological archives need the images to be compressed at a moderate compression ratio between 10:1 to 20:1 while retaining good diagnostic quality. We have developed a compression algorithm based on discrete wavelet transforms (DWTs) and arithmetic coding (AC) that satisfies those requirements. This new method is superior to the previously developed full frame discrete cosine transform (FFDCT) method, as well as the industrial standard developed by the joint photographic expert group (JPEG). Since DWT is localized in both spatial and scale domains, the error due to quantization of coefficients does not propagate throughout the reconstructed picture as in FFDCT. Because it is a global transformation, it does not suffer the limitation of block transform methods such as JPEG. The severity of the error as measured by the normalized mean square error (NMSE) and maximum difference technique increases very slowly with compression ratio compared to the FFDCT. Normalized nearest neighbor difference (NNND), which is a measure of blockiness, stays approximately constant, while JPEG NNND increases rapidly with compression ratio. Furthermore, DWT has an efficient finite response filter FlR implementation that can be put in parallel hardware. DWT also offers total flexibility in the image format; the size of the image does not have to be a power of two as in the case of FFDCT