Skip to Main Content
There are many industrial, biomedical, and military applications for which there is a need to compress images using techniques that are as closely information-preserving as possible. The fact that images cannot be accurately modeled as stationary processes and that the image statistics are usually not Gaussian reduces the efficiency of commonly used predictive and transform-based coding procedures. An efficient multiple-source coding procedure capable of preserving sharp features in the image with small computational load is presented. The procedure uses a composite model to deal with the nonstationarity in the space variation of the mean function. The image is then modeled as the addition of two components: an "approximate" image, which represents the underlying structure of the image, and a "difference" image, which corresponds to random variations superimposed on that structure. The approximate image is constructed using a binary image that describes the boundaries of its homogeneous regions, the local means of these regions, and an interpolation between the regions. The difference image is the result of subtracting the approximate image from the original. The coding of the binary and the difference images can be done very efficiently: the binary image can be coded by a facsimile technique, and the difference image can be coded using a predictive technique since it can be modeled accurately as a stationary random field. A lower bound for the compressibility of an image using the proposed procedure is given. To illustrate the procedure, as well as some issues involved, an example is shown.