Results 271 to 280 of about 21,396 (309)

Alphabet-constrained vector quantization

IEEE Transactions on Information Theory, 1993
Summary: Alphabet-constrained rate-distortion theory is extended to coding of sources with memory. Two different cases are considered: one, when only the size of the codebook is constrained and additionally, when the codevector values are also held fixed.
R. Padmanabha Rao, William A. Pearlman
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Fractal dimension and vector quantization

Information Processing Letters, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Krishna Kumaraswamy, Vasileios Megalooikonomou, Christos Faloutsos   +2 more
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Lapped orthogonal vector quantization

Proceedings of Data Compression Conference - DCC '96, 2002
The blocking artifacts that arise in the use of traditional vector quantization (VQ) schemes can, in general, be virtually eliminated via an efficient lapped VQ strategy. With lapped VQ, blocks are obtained from the source in an overlapped manner, and reconstructed via superposition of overlapped codevectors.
Henrique S. Malvar, Gary J. Sullivan, Gregory W. Wornell   +2 more
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Fast fuzzy vector quantization

International Conference on Fuzzy Systems, 2010
In this paper we introduce a novel fuzzy vector quantization algorithm that tries to solve certain problems related to the implementation of fuzzy cluster analysis in vector quantization. The proposed method employs an objective function that combines the merits of fuzzy and crisp clustering in a uniform fashion.
George E. Tsekouras, Dimitrios Paris Darzentas, Ioanna Drakoulaki, Antonios D. Niros   +3 more
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Vector quantization of neural networks

IEEE Transactions on Neural Networks, 1998
The problem of vector quantizing the parameters of a neural network is addressed, followed by a discussion of different algorithms applicable for quantizer design. Optimal, as well as several suboptimal quantization schemes are described. Simulations involving nonlinear prediction of speech signals are presented to compare the performance of different ...
W. C. Chu, N. K. Bose
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On the training distortion of vector quantizers

IEEE Transactions on Information Theory, 2000
Summary: The in-training-set performance of a vector quantizer as a function of its training set size is investigated. For squared error distortion and independent training data, worst case type upper bounds are derived on the minimum training distortion achieved by an empirically optimal quantizer.
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Gauss mixture vector quantization

2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2002
Gauss mixtures are a popular class of models in statistics and statistical signal processing because they can provide good fits to smooth densities, because they have a rich theory, and because they can be well estimated by existing algorithms such as the EM (expectation maximization) algorithm. We here extend an information theoretic extremal property
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Vector quantization

2019
Vector quantization (VQ) is a critical step in representing signals in digital form for computer processing. It has various uses in signal and image compression and in classification. If the signal samples are quantized separately, the operation is called “scalar quantization.” Consequently, if the samples are grouped to form vectors, their ...
Çetin, A. Enis, Gerek, Ö. N.
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Vector quantization

IEEE ASSP Magazine, 1984
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Residual Vector Product Quantization for approximate nearest neighbor search

Expert Systems With Applications, 2023
Lushuai Niu, Zhi Xu, Daojing He
exaly