Skip to main content
Article
Index-compressed vector quantisation based on index mapping
Faculty of Informatics - Papers (Archive)
  • Jamshid Shanbehzadeh, Tarbiat Moalem University, Islamic Republic of Ira
  • Philip Ogunbona, University of Wollongong
RIS ID
65863
Publication Date
1-1-1997
Publication Details

Shanbehzadeh, J. & Ogunbona, P. (1997). Index-compressed vector quantisation based on index mapping. IEE Proceedings: Vision, Image and Signal Processing, 144 (1), 31-38.

Abstract

The authors introduce a novel coding technique which significantly improves the performance of the traditional vector quantisation (VQ) schemes at low bit rates. High interblock correlation in natural images results in a high probability that neighbouring image blocks are mapped to small subsets of the VQ codebook, which contains highly correlated codevectors. If, instead of the whole VQ codebook, a small subset is considered for the purpose of encoding neighbouring blocks, it is possible to improve the performance of traditional VQ schemes significantly. The performance improvement obtained with the new method is about 3dB on average when compared with traditional VQ schemes at low bit rates. The method provides better performance than the JPEG coding standard at low bit rates, and gives comparable results with much less complexity than address VQ.

Citation Information
Jamshid Shanbehzadeh and Philip Ogunbona. "Index-compressed vector quantisation based on index mapping" (1997) p. 31 - 38
Available at: http://works.bepress.com/p_ogunbona/83/