Lossless compression of multi-dimensional medical image data using binary-decomposed high-order entropy coding

Information theory indicates that coding efficiency can be improved by utilizing high-order entropy coding (HOEC). However, serious implementation difficulties limit the practical value of HOEC for grayscale image compression. We present a new approach, called binary-decomposed (BD) high-order entropy coding, that significantly reduces the complexity of the implementation and increases the accuracy in estimating the statistical model. In this approach a grayscale image is first decomposed into a group of binary sub-images, each corresponding to one of the gray levels. When HOEC is applied to these sub-images instead of the original image, the subsequent coding is made simpler and more accurate statistically. We apply this coding technique in lossless compression of medical images and imaging data, and demonstrate that the performance advantage of this approach is significant