Sample weighting when training self-organizing maps for image compression
Jari A. Kangas · 2002
Image compression is an essential task for image storage and transmission applications. Vector quantization is often used when high compression rates are needed. Self-organizing map (SOM) algorithm can be used to generate codebooks for vector quantization. Previously it has been demonstrated that using the special property of the SOM algorithm that the codebook entries are ordered one can use prediction coding of codewords to make the compression more effective. In this paper it is shown that training the SOM algorithm by using different weighting for sample blocks having different statistical characteristics one can further increase the compression efficiency.