Variable Length Tree-Structured Subvector Quantization
نویسندگان
چکیده
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 signiicantly sacriicing coding performance. The greedy tree growing algorithm for the design of such a vector quantizer codebook is outlined. Diierent 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 tranformation to the source and the implication of deening the subvectors from orthogonal subspaces are also discussed. Finally simulation results on still images and AR(1) source are presented to connrm our propositions.
منابع مشابه
Variable-length constrained-storage tree-structured vector quantization
Constrained storage vector quantization, (CSVQ), introduced by Chan and Gersho (1990, 1991) allows for the stagewise design of balanced tree-structured residual vector quantization codebooks with low encoding and storage complexities. On the other hand, it has been established by Makhoul et al. (1985), Riskin et al. (1991), and by Mahesh et al. (see IEEE Trans. Inform. Theory, vol.41, p.917-30,...
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