A Growing and Splitting Elastic Network for Vector Quantization

{ A new vector quantization method is proposed which incrementally generates a suitable codebook. During the generation process new vectors are inserted in areas of the input vector space where the quantization error is especially high. A one-dimensional topological neighborhood makes it possible to interpolate new vectors from existing ones. Vectors not contributing to error minimization are removed. After the desired number of vectors is reached, a stochastic approximation phase ne tunes the codebook. The nal quality of the codebooks is exceptional. A comparison with two well-known methods for vector quantization was performed by solving an image compression problem. The results indicate that the new method is clearly superior to both other approaches.