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Sample weighting when training self-organizing maps for image compression

Content Provider	IEEE Xplore Digital Library
Author	Kangas, J.
Copyright Year	1995
Description	Author affiliation: Neural Networks Res. Centre, Helsinki Univ. of Technol., Espoo, Finland (Kangas, J.)
Abstract	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.
Starting Page	343
Ending Page	350
File Size	447999
Page Count	8
File Format	PDF
ISBN	078032739X
DOI	10.1109/NNSP.1995.514908
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-08-31
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Self organizing feature maps Image coding Vector quantization Pixel Image storage Clustering algorithms Iterative algorithms Neural networks Decoding Algorithm design and analysis
Content Type	Text
Resource Type	Article

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