• Lagrangian optimization of two...

Treffer: Lagrangian optimization of two-description scalar quantizers

Title:
Lagrangian optimization of two-description scalar quantizers
Authors:
DUMITRESCU, Sorina, XIAOLIN WU
Source:
IEEE transactions on information theory. 53(11):3990-4012
Publisher Information:
New York, NY: Institute of Electrical and Electronics Engineers, 2007.
Publication Year:
2007
Physical Description:
print, 25 ref
Original Material:
INIST-CNRS
Subject Terms:
Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie de l'information, Information theory, Théorie du signal et des communications, Signal and communications theory, Codage, codes, Coding, codes, Echantillonnage, quantification, Sampling, quantization, Algorithme, Algorithm, Algoritmo, Codage source, Source coding, Compression signal, Signal compression, Compresión señal, Conception optimale, Optimal design, Concepción optimal, Convexité, Convexity, Convexidad, Evaluation performance, Performance evaluation, Evaluación prestación, Fonction convexe, Convex function, Función convexa, Fonction objectif, Objective function, Función objetivo, Monotonie, Monotonicity, Monotonía, Multiplicateur Lagrange, Lagrange multiplier, Multiplicador Lagrange, Optimisation, Optimization, Optimización, Performance algorithme, Algorithm performance, Resultado algoritmo, Programmation dynamique, Dynamic programming, Programación dinámica, Quantificateur, Quantifier, Cuantificador, Quantification signal, Signal quantization, Cuantificación señal, Stratégie recherche, Search strategy, Estrategia investigación, Taux perte, Loss rate, Porcentaje pérdida, Signal source réparti, Distributed source signal, Señal fuente distribuida, Convexity of quantizer cells, Lagrangian optimization, distributed source coding, minimum-weight k-edge path, multiple description quantization (MDQ)
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada
ISSN:
0018-9448
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=19204434
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Telecommunications and information theory
Accession Number:
edscal.19204434
Database:
PASCAL Archive

Weitere Informationen

-In this paper, we study the problem of optimal design of balanced two-description fixed-rate scalar quantizer (2DSQ) under the constraint of convex codecells. Using a graph-based approach to model the problem, we show that the minimum expected distortion of the 2DSQ is a convex function of the number of codecells in the side quantizers. This property allows the problem to be solved by Lagrangian minimization for which the optimal Lagrangian multiplier exists. Given a trial multiplier, we exploit a monotonicity of the objective function, and develop a simple and fast dynamic programming technique to solve the parameterized problem. To further improve the algorithm efficiency, we propose an RD-guided search strategy to find the optimal Lagrangian multiplier. In our experiments on distributions of interest for signal compression applications the proposed algorithm improves the speed of the fastest algorithm so far, by a factor of O(K/log K), where K is the number of codecells in each side quantizer. We also assess the impact on the optimality of the convex codecell constraint. Using a published performance analysis of 2DSQ at high rates, we show that asymptotically this constraint does not preclude optimality for L2 distortion measure, when channels have a higher than 0.12 loss rate.