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 fine tunes the codebook. The final quality of the codebooks is exceptional. A comparison with two methods for vector quantization is performed by solving an image compression problem. The results indicate that the new method is clearly superior to both other approaches.<
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