Results 261 to 270 of about 21,396 (309)

A pyramid vector quantizer

IEEE Transactions on Information Theory, 1986
The geometric properties of a memoryless Laplacian source are presented and used to establish a source coding theorem. Motivated by this geometric structure, a pyramid vector quantizer (PVQ) is developed for arbitrary vector dimension. The PVQ is based on the cubic lattice points that lie on the surface of an L-dimensional pyramid and has simple ...
openaire   +2 more sources

Extension of two-stage vector quantization-lattice vector quantization

IEEE Transactions on Communications, 1997
This paper is the extension of two-stage vector quantization-(spherical) lattice vector quantization (VQ-(S)LVQ) recently introduced by Pan and Fischer (see IEEE Trans. Inform. Theory, vol.41, p.155, 1995). First, according to high resolution quantization theory, generalized vector quantization-lattice vector quantization (G-VQ-LVQ) is formulated in ...
openaire   +1 more source

Optimal vector transform for vector quantization

IEEE Signal Processing Letters, 1994
The vector transform has previously been proposed for vector quantization image coding. It is intended for decorrelating intervector dependency and preserving intravector dependency. The present authors study optimally such a transform. It is found that the optimal vector transform in the above senses, with the first-order Gaussian Markov model, does ...
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.
openaire   +1 more source

CONSTRAINED LEARNING VECTOR QUANTIZATION

International Journal of Neural Systems, 1994
Kohonen’s learning vector quantization (LVQ) is an efficient neural network based technique for pattern recognition. The performance of the method depends on proper selection of the learning parameters. Over-training may cause a degradation in recognition rate of the final classifier. In this paper we introduce constrained learning vector quantization
openaire   +2 more sources

Convergence of Vector Quantizers with Applications to Optimal Quantization

SIAM Journal on Applied Mathematics, 1984
Summary: Suppose that a sequence of probability distribution functions \(\{F_ n\}\) converges weakly to a distribution function F. Does the sequence of optimal quantizers for the \(F_ n's\) converge to an optimal quantizer for F? If so, do the respective distortions converge to the optimal distortion for F?
Abaya, Efren F., Wise, Gary L.
openaire   +1 more source

Vector quantization with edge reconstruction

Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101), 2000
This paper proposes a vector quantization (VQ) scheme that improves the quality of the reconstructed image by correcting quantization artifacts. The basic idea of the coding scheme is to treat separately the blocks containing edges (edge blocks), as they contain important perceptual information.
Razvan Iordache, Azeddine Beghdadi, Ioan Tabus   +2 more
openaire   +1 more source

Vector quantization of the articulatory space

IEEE Transactions on Acoustics, Speech, and Signal Processing, 1988
A technique is presented for quantizing the articulatory space, i.e. for replacing the continuum of all possible vocal tract shapes by a finite set of shapes that span the articulatory space. Vocal tract shapes are represented as vectors of parameters that control an articulatory model, so vector quantization are applicable for deriving this codebook ...
Jerry N. Larar, Juergen Schroeter, Man Mohan Sondhi   +2 more
openaire   +2 more sources

Hopfield Networks for Vector Quantization

2020
We consider the problem of finding representative prototypes within a set of data and solve it using Hopfield networks. Our key idea is to minimize the mean discrepancy between kernel density estimates of the distributions of data points and prototypes.
Christian Bauckhage, Rajkumar Ramamurthy, Rafet Sifa   +2 more
openaire   +1 more source

Distributed Detection With Vector Quantizer

IEEE Transactions on Signal and Information Processing over Networks, 2016
Motivated by distributed inference over big datasets problems, we study multiterminal distributed inference problems when each terminal employs vector quantizer. The use of vector quantizer enables us to relax the conditional independence assumption normally used in the distributed detection with scalar quantizer scenarios.
Wenwen Zhao, Lifeng Lai
openaire   +1 more source