27 February 1996 Variable-length tree-structured subvector quantization
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Proceedings Volume 2727, Visual Communications and Image Processing '96; (1996) https://doi.org/10.1117/12.233245
Event: Visual Communications and Image Processing '96, 1996, Orlando, FL, United States
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.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ulug Bayazit, Ulug Bayazit, William A. Pearlman, William A. Pearlman, } "Variable-length tree-structured subvector quantization", Proc. SPIE 2727, Visual Communications and Image Processing '96, (27 February 1996); doi: 10.1117/12.233245; https://doi.org/10.1117/12.233245
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