Abstract
Vector Quantization (VQ) is a powerful technique for image compression but its coding complexity may be an important drawback. Self-Organizing Maps (SOM) are well suited for topologically ordered codebook design. We propose to use that topology for reducing image coding time. Using inter-block correlations, the nearest neighbor search is restricted to the neighborhood of the precedingly used code vector instead of the entire codebook. We obtained a reduction of up to 84% in the coding time compared to full search.
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Foucher, C., Le Guennec, D., Vaucher, G. (2002). Kohonen Maps Applied to Fast Image Vector Quantization. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_210
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DOI: https://doi.org/10.1007/3-540-46084-5_210
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