VECTOR QUANTIZATION OF IMAGES USING A FUZZY CLUSTERING METHOD

Abstract

A novel approach with an incremental fuzzy clustering algorithm for vector quantization of images is proposed. For compression, a given image is divided into blocks of training vectors, and fuzzy clusters are generated progressively from those vectors. The obtained fuzzy clusters become the code-book from which the entire image is transformed into a sequence of indices. For decompression, the indices are decoded and the image is reconstructed. The advantages of our approach are that clusters generated are compact and dense, the real distribution of training vectors can be captured, and training vectors can be represented by prototyping clusters more appropriately. Experimental results have shown that our method can achieve a higher compression ratio and produce a smaller error than other methods.