Lossless Medical Image Compression by Integer Wavelet and Predictive Coding
Table 1
Comparison of available techniques.
Predictive-based coding methods
Comments
LJPEG
Predictive algorithm used Huffman or arithmetic entropy algorithm Highest compression ratio
JPEG LS
Nearly lossless High speed and compression ratio, mostly used JPEG-LS algorithm is more scalable than JPEG and JPEG 2000
JPEG 2000
Wavelet-based method High noise compensation ratio JPEG 2000 delivers a typical compression gain in the range of 20%, depending on the image characteristics Higher-resolution images tend to benefit more, where JPEG-2000’s spatial-redundancy prediction can contribute more to the compression process JPEG 2000 has quality advantage over JPEG
MED
Belongs to the group of switching predictors MMSE performs the adaptation prediction coefficient on the basis of a training set of causal pixels this approach can achieve better results Redundancy between frames is reduced by the prediction of each pixel based
GAP
Gradient estimation around the current pixel Gradient estimation is estimated by the context of current pixel
GED
GED predictor is a simple combination of gradient and median predictors Pixel in context of horizontal edge, vertical edge or smooth region
CALIC
Poor performance High-compression ratio More complex algorithm with more resources
Blending predictor (model based)
Models for particular pixel designed by combination of linear subpredictors Bayesian model averaging used (BMA), risk associated with this model Better performance
