Results 261 to 270 of about 18,574 (304)

Constrained Vector Quantization

1992
There is no better way to quantize a single vector than to use VQ with a codebook that is optimal for the probability distribution describing the random vector. However, direct use of VQ suffers from a serious complexity barrier that greatly limits its practical use as a complete and self-contained coding ...
Allen Gersho, Robert M. Gray
openaire   +1 more source

Spectrum vector quantization

2008 7th World Congress on Intelligent Control and Automation, 2008
This paper proposes a new spectrum vector quantization algorithm (SVQ). SVQ conducts vector quantization in spectrum space. It is characterized by some novels. The first is the informed initialization of prototypes, which is achieved by a modified support vector clustering procedure. The second is the SVD-based spectrum analysis. This technique employs
null Ping Ling, null Dajin Gao, null Xiangyang You, null Xiangsheng Rong, null Ming Xu   +4 more
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Predictive vector quantization

ICASSP '84. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005
A proposed class of vector quantizers with memory, called Predictive Vector Quantizers (PVQ), exhibits good performance (as measured by mean-squared error distortion) for the encoding of speech signals at 16 kbits/sec. Two methods for designing PVQ's are described, and their performance is compared to that of memoryless VQ's by computer simulation ...
A. Haoui, D. Messerschmitt
openaire   +1 more source

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
openaire   +2 more sources

Adaptive Vector Quantization

1992
In adaptive quantization, the parameters of a quantizer are updated during real-time operation based on observed information regarding the statistics of the signal being quantized. This paper first discusses single parameter backward adaptation for scalar and vector quantization.
Allen Gersho, Robert M. Gray
openaire   +1 more source

Symmetric trellis-coded vector quantization

IEEE Transactions on Communications, 1997
We present here design techniques for trellis-coded vector quantizers with symmetric codebooks that facilitate low-complexity quantization as well as partitioning into equiprobable sets for trellis coding. The quantization performance of this coder on the independently identically distributed (i.i.d.) Laplacian source matches the performance of trellis-
B. Belzer, J.D. Villasenor
openaire   +1 more source

Predictive Vector Quantization

1992
The coverage of VQ has focused thus far on the coding of a single vector extracted from a signal, that is, on memoryless VQ where each input vector is coded in a manner that does not depend on past (or future) actions of the encoder or decoder. This vector is typically a set of parameters extracted from a finite segment of a signal or a set of adjacent
Allen Gersho, Robert M. Gray
openaire   +1 more source

Filtering and Searching Vector Quantization

Journal of VLSI signal processing systems for signal, image and video technology, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Viral vector platforms within the gene therapy landscape

Signal Transduction and Targeted Therapy, 2021
Phillip W L Tai, Guangping Gao
exaly  

Vector quantization

IEEE ASSP Magazine, 1984
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