Results 331 to 340 of about 1,866,673 (365)

Bennett's integral for vector quantizers

IEEE Transactions on Information Theory, 1995
Summary: This paper extends \textit{W. R. Bennett}'s integral [Bell. Syst. Tech. J. 27, 446-472 (1948)]\ from scalar to vector quantizers, giving a simple formula that expresses the \(r\)th-power distortion of a many-point vector quantizer in terms of the number of points, point density function, inertial profile, and the distribution of the source ...
David L. Neuhoff, Sangsin Na
openaire   +2 more sources

Predictive classified vector quantization

IEEE Transactions on Image Processing, 1992
A vector quantization scheme based on the classified vector quantization (CVQ) concept, called predictive classified vector quantization (PCVQ), is presented. Unlike CVQ where the classification information has to be transmitted, PCVQ predicts it, thus saving valuable bit rate.
H.C. Koh, King Ngi Ngan
openaire   +3 more sources

Fast video vector quantization

2008 23rd International Symposium on Computer and Information Sciences, 2008
In this paper fast video vector quantization algorithm was developed utilizing high correlation between successive frames which can be used for motion compensation as a next step. Best matching code vector search speed is increased using previously encoded frame code vector index list, which carry the available information of that frame.
Yakut, Mehmet, Reisoglu, Neval
openaire   +3 more sources

Trellis-coded vector quantization

IEEE Transactions on Information Theory, 1991
Trellis-coded quantization is generalized to allow a vector reproduction alphabet. Three encoding structures are described, several encoder design rules are presented, and two design algorithms are developed. It is shown that for a stationary ergodic vector source, if the optimized trellis-coded vector quantization reproduction process is jointly ...
M. Wang, Thomas R. Fischer, Michael W. Marcellin   +2 more
openaire   +2 more sources

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   +3 more sources

Techniques for Vector Quantization.

1984
Abstract : The second year of AFOSR support at the University of California, Santa Barbara has allowed us to make significant strides in exploring the potential of vector quantization for source coding. Some of this work is described in the attached list of references.
openaire   +2 more sources

Vector quantization and signal compression

The Kluwer International Series in Engineering and Computer Science, 1991
A. Gersho, R. Gray
semanticscholar   +1 more source

Vector Quantization

ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2017
R. Gray
semanticscholar   +1 more source

Advances and Open Problems in Federated Learning

Foundations and Trends in Machine Learning, 2021
Han Yu, Ana Cecilia Boetto
exaly  

Vector Quantization

Algorithmic Complexity and communication problems, 2020
J. Barthélemy, G. Cohen, A. Lobstein
semanticscholar   +1 more source