A fuzzy vector quantization approach to image compression

The use of fuzzy clustering analysis in the early stages of a vector quantization process is able to make this process less sensitive to initialization. This is justified by the fact that fuzzy clustering provides a framework for the quantitative formulation of the uncertainty typically involved in a training vector space. This paper proposes a fuzzy clustering based vector quantization algorithm, which employs an effective vector assignment strategy for the transition from fuzzy mode, where each training vector is assigned to more than one clusters, to crisp mode, where each training vector is assigned to only one cluster. This transition is controlled by analytical conditions that are obtained by minimizing a modified objective function for the fuzzy c-means algorithm. The application to image compression shows that the proposed approach is able to achieve a very efficient performance, while maintaining the computational capabilities of other methods reported in the literature.