Jigsaw-puzzle vector quantization for image compression

Chia Hung Yeh*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


A new finite-state vector quantization scheme called jigsaw-puzzle vector quantization (JPVQ) is proposed to provide better image quality, especially in the low bit rate context. For low bit rate image coding with conventional finite-state vector quantization (FSVQ) techniques, image quality degrades due to error propagation from one state to the next. The proposed JPVQ algorithm exploits the four-step side-match prediction technique to optimize the spatial continuity of each encoded block to improve the coding performance and reduce the error propagation effect. In the proposed coding scheme, an input block can be encoded by the jigsaw-puzzle block, the dynamic codebook, or the super-codebook. It is demonstrated with experimental results that JPVQ performs significantly better than traditional FSVQ techniques.

Original languageEnglish
Pages (from-to)363-370
Number of pages8
JournalOptical Engineering
Issue number2
Publication statusPublished - 2004 Feb
Externally publishedYes


  • Classified side-match vector quantization
  • Dynamic finite-state vector quantization
  • Finite-state vector quantization
  • Gradient classified side-match vector quantization
  • Gradient-match vector quantization
  • Image compression
  • Improved feature match finite-state vector quantization
  • Side-match vector quantization
  • Vector quantization

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • General Engineering


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