Image Compression Using Self-Organization Networks

Oscal T.C. Chen, Bing J. Sheu

Research output: Contribution to journalJournal Article peer-review

43 Scopus citations


A self-organization neural network architecture is used to implement vector quantization for image compression. A modified self-organization algorithm, which is based on the frequency-sensitive cost function and centroid learning rule, is utilized to construct the codebooks. Performances of this frequency-sensitive self-organization network and a conventional algorithm for vector quantization are compared. The proposed method is quite efficient and can achieve near-optimal results. Good adaptivity for different statistics of source data can also be achieved.

Original languageEnglish
Pages (from-to)480-489
Number of pages10
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number5
StatePublished - 10 1994
Externally publishedYes


  • Image processing
  • network
  • neural
  • vector quantization


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