High-order entropy coding of medical image data using different binary-decomposed representations

Information theory indicates that the 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