Image compression based on fuzzy segmentation and anisotropic diffusion

In this paper we present a hybrid model for image compression based on fuzzy segmentation and Partial Differential Equations. The main motivation behind our approach is to produce immediate access to objects/features of interest in a high quality decoded image which could be useful on smart devices, for analysis purpose, as well as for multimedia content-based description standards. The image is approximated as a set of uniform regions: The technique will assign well-defined members to homogenous regions in order to achieve image segmentation. The fuzzy c-means (FcM) is a guide to cluster image data. A second stage coding is applied using entropy coding to remove the whole image entropy redundancy. In the decoding phase, we suggest the application of a nonlinear anisotropic diffusion to enhance the quality of the coded image.