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Proceedings Paper

Variable-length tree-structured subvector quantization
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Paper Abstract

It is demonstrated in this paper that the encoding complexity advantage of a variable-length tree-structured vector quantizer (VLTSVQ) can be enhanced by encoding low dimensional subvectors of a source vector instead of the source vector itself at the nodes of the tree structure without significantly sacrificing coding performance. The greedy tree growing algorithm for the design of such a vector quantizer codebook is outlined. Different ways of partitioning the source vector into its subvectors and several criteria of interest for selecting the appropriate subvector for making the encoding decision at each node are discussed. Techniques of tree pruning and resolution reduction are applied to obtain improved coding performance at the same low encoding complexity. Application of an orthonormal transformation such as KLT or subband transformation to the source and the implication of defining the subvectors from orthogonal subspaces are also discussed. Finally simulation results on still images and AR(1) source are presented to confirm our propositions.

Paper Details

Date Published: 27 February 1996
PDF: 11 pages
Proc. SPIE 2727, Visual Communications and Image Processing '96, (27 February 1996); doi: 10.1117/12.233245
Show Author Affiliations
Ulug Bayazit, Rensselaer Polytechnic Institute (United States)
William A. Pearlman, Rensselaer Polytechnic Institute (United States)

Published in SPIE Proceedings Vol. 2727:
Visual Communications and Image Processing '96
Rashid Ansari; Mark J. T. Smith, Editor(s)

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