Storage- and entropy-constrained classified vector quantization

Wen Jyi Hwang*, Yue Shen Tu, Yeong Cherng Lu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


This paper presents a novel variable-rate classified vector quantizer (CVQ) design algorithm for the applications of image coding. The design algorithm, termed storage- and entropy-constrained classified vector quantization (SECCVQ) algorithm, is able to control the rate and the storage size of the CVQ. The algorithm allocates the rate and storage size available to each class of the VQ optimally so that the average distortion of the SECCVQ is minimized. The classification of image blocks is based on the edge orientation of each block in the wavelet domain. To reduce the arithmetic complexity of the CVQ, we employ a novel partial distance codeword search algorithm in the wavelet domain. Simulation results show that the SECCVQ enjoys low average distortion, low encoding complexity, high visual perception quality, and is well-suited for very low bit rate image coding.

Original languageEnglish
Pages (from-to)1252-1261
Number of pages10
JournalIEEE Transactions on Consumer Electronics
Issue number4
Publication statusPublished - 1997
Externally publishedYes


  • Fast codeword search
  • Vector quantization
  • Wavelet transform

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering


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