Optimization of the index assignments for multiple description vector quantizers
@article{Goertz2003OptimizationOT,
title={Optimization of the index assignments for multiple description vector quantizers},
author={Norbert Goertz and Pornchai Leelapornchai},
journal={IEEE Trans. Commun.},
year={2003},
volume={51},
pages={336-340}
}The optimization criterion and a practically feasible new algorithm is stated for the optimization of the index assignments of a multiple-description unconstrained vector quantizer with an arbitrary number of descriptions. In the simulations, the index-optimized multiple-description vector quantizer achieves significant gains in source signal-to-noise ratio over scalar multiple description schemes.
48 Citations
On index assignment and the design of multiple description quantizers
- Computer Science2004 IEEE International Conference on Acoustics, Speech, and Signal Processing
- 2004
The practical design of multiple description quantizers for diversity-based communication is investigated. A simulated annealing based method is proposed for obtaining the optimal index assignment…
Design of n-Channel Multiple Description Vector Quantizers
- Computer ScienceConference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005.
- 2005
A new design approach for multiple description vector quantizers over more than two channels using only one decoder is presented, inspired by the concept of channel optimized vector quantization.
Multiple Description Quantizer Design for Space-time Orthogonal Block Coded Channels
- Computer Science2007 IEEE International Symposium on Information Theory
- 2007
A scheme to optimize multiple description vector quantizers for space-time orthogonal block coded slow Rayleigh fading channels using an upper bound of the channel transition probability achieved by the MAP decoder is proposed.
INDEX MAPPING FOR ROBUST MULTIPLE DESCRIPTION LATTICE VECTOR QUANTIZER
- Computer Science
- 2015
This thesis considers the construction of robust multiple description lattice vector quantizers (MDLVQ) by addressing the problem of designing an index mapping able to combat bit errors by proposing two methods to tackle this problem.
Multiple description quantizer design for multiple-antenna systems with MAP detection
- Computer ScienceIEEE Transactions on Communications
- 2010
The proposed scheme of combining multiple description coding and space-time orthogonal block coding is shown to be superior to each individual coding scheme.
Design of low-resolution multiple description vector quantizers by means of the Self Organizing Maps
- Computer Science2008 16th European Signal Processing Conference
- 2008
This work shows that good multiple description VQ codebooks can be designed in a simple and efficient way by resorting to the Self-Organizing Maps algorithm, where just a few parameters must be reasonably selected.
Self-organizing maps for the design of multiple description vector quantizers
- Computer ScienceNeurocomputing
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Low-Complexity Multiple Description Vector Quantization With Constrained Central Codebook
- Computer Science2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings
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Two central-codebook-constrained MDVQ (CMDVQ) schemes are proposed to reduce the storage and search complexity and simulation results show that for low channel loss rates, a tradeoff exists between choosing CMDV Q for its low complexity and the conventional MDVZ for its higher signal-to-noise ratio (SNR) performance.
Multiple Description Image Coding Based on Multi-Stage Vector Quantization
- Computer Science2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers
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A new design approach for multiple description coding, based on multi-stage vector quantizers, is presented. The design is not limited to systems with two descriptions, but is also well suited for…
MULTIPLE DESCRIPTION LATTICE VECTOR QUANTIZATION MULTIPLE DESCRIPTION LATTICE VECTOR QUANTIZATION
- Computer Science
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It is proved, under the assumption of high resolution, that the algorithm is optimal for K = 2, and the optimality holds for many commonly used good lattices of any dimensions, over the entire range of achievable central distortions given the side entropy rate.
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