An Optimal VQ Codebook Design Using the Co-adaptation of Learning and Evolution
This paper proposes a design method of an optimal VQ (Vector Quantization) codebook using the co-adaptation of self-organizing maps that attempts to incorporates the Kohonen’s learning into the GA evolution. The Kohonen’s learning rule used for vector quantization of images is sensitive to the choice of its initial parameters and the resultant codebook does not guarantee a minimum distortion. We alleviate these problems by co-adapting the codebooks by evolution and learning in a way that the evolution performs the global search and makes inter-codebook adjustments by altering the codebook structures while the learning performs the local search and makes intra-codebook adjustments by making each codebook’s distortion small. Simulation results show that the evolution guided by a local learning provides the fast convergence, the co-adapted codebook produces better reconstruction image quality than the non-learned equivalent, and Lamarckian co-adaptation turns out more appropriate for the VQ problem.
KeywordsVector Quantization Training Vector Distortion Measure Voronoi Region Average Distortion
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