Vector quantization with hierarchical classification of sub-blocks

Jorma Virtamo, Seppo Valli

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

A hierarchical classified vector quantization (HCVQ) method is described. In this method, the image is coded in several steps, starting with a relatively large block size, and successively dividing the block into smaller sub-blocks in a quad-tree fashion. The initial block is first vector quantized in the normal way. Classified vector quantization is then performed for its sub-blocks using the vector index of the initial block, i.e. rough information of the image, and the location of the sub-block within the initial block as classifiers. The coding proceeds in a similar way, adding more information of the fine details at each level. The method is found to be effective and to give a good subjective quality. It is also simple to implement, leading to coding speeds typical to a tree search VQ.
Original languageEnglish
Pages (from-to)475-479
JournalSignal Processing: Image Communication
Volume8
Issue number6
DOIs
Publication statusPublished - Sep 1996
MoE publication typeA1 Journal article-refereed

Keywords

  • image coding
  • vector quantization
  • multiresolution
  • hierarchical

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