Distortion Sensitive Competitive Learning for vector quantizer design

Sze Tsan Choy, Wan Chi Siu

Research output: Journal article publicationConference articleAcademic researchpeer-review

6 Citations (Scopus)


In this paper, we propose the Distortion Sensitive Competitive Learning (DSCL) algorithm for codebook design in image vector quantization. The algorithm is based on the equidistortion principle for asymptotically optimal vector quantizer after Gersho (1979) and recently from Ueda and Nakano (1994). The DSCL is simple and efficient in that a single weight vector update is performed per training vector, and the processing speed of the DSCL on sequential or multiprocessor environment can further be improved by applying a modified partial distance elimination (MPDE) method. Simulations indicate that the DSCL outperforms some recently proposed neural algorithms, including the `Neural-Gas' from Martinetz et al. (1993) and the DEFCL from Butler and Jiang (1996). In combining with the MPDE, the DSCL is faster than the `Neural-Gas' up to a factor of 45 times on a sequential machine, and yet arrives at better code-books with the same number of iterations.
Original languageEnglish
Pages (from-to)3405-3408
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publication statusPublished - 1 Jan 1997
EventProceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 5) - Munich, Germany
Duration: 21 Apr 199724 Apr 1997

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Acoustics and Ultrasonics


Dive into the research topics of 'Distortion Sensitive Competitive Learning for vector quantizer design'. Together they form a unique fingerprint.

Cite this