Joint source-channel coding using tree-strctured vector quantization for remote sensing images
Joint source-channel coding using tree-strctured vector quantization for remote sensing images

Author:

Publisher:

Published: 2000

Total Pages:

ISBN-13:

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Este trabalho estuda o problema de compressão de imagens de sensoriamento remoto segundo a ótica da codificação conjunta fonte-canal. É analisado o desempenho de métodos baseados em quantização vetorial segundo o algoritmo LBG, principalmente o COVQ (Channel Optimized Vector Quantizer) bem como a quantização vetorial estruturada em árvore. Dentro desse contexto, são propostos 2 novos métodos para a resolução do problema: (1)Uma quantização vetorial estruturada em árvores que leva em conta a transmissão através de canais ruidosos, solução denominada COTSVQ (Channel-Design Tree Strutured Vecotr Quantizer), bem como (2) uma classe de métodos que se utiliza de códigos corretores de erro sobre a estrutura progressiva do TSVQ, de forma a proteger os dados de forma ativa durante a transmissão. Os dois métodos propostos podem ser combinados no mesmo compressor, de forma a originar uma classe ampla de compressores adaptados à transmissão por canais com ruído. São apresentados resultados que comparam os desempenhos dos métodos propostos com aqueles já existentes para uma análise de desempenho, na situação de transmissão via satélite de imagens captadas e comprimidas para uma taxa de 1,5bpp. Os resultados mostram que os métodos propostos são muito menos complexos que os já existentes, porém conseguindo atingir uma qualidade de imagem equivalente, ou, em alguns casos, superior.

Joint Source-channel Coding Of Discrete-time Signals With Continuous Amplitudes
Joint Source-channel Coding Of Discrete-time Signals With Continuous Amplitudes

Author: Norbert Goertz

Publisher: World Scientific

Published: 2007-09-21

Total Pages: 207

ISBN-13: 1908979143

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This book provides the first comprehensive and easy-to-read discussion of joint source-channel encoding and decoding for source signals with continuous amplitudes. It is a state-of-the-art presentation of this exciting, thriving field of research, making pioneering contributions to the new concept of source-adaptive modulation.The book starts with the basic theory and the motivation for a joint realization of source and channel coding. Specialized chapters deal with practically relevant scenarios such as iterative source-channel decoding and its optimization for a given encoder, and also improved encoder designs by channel-adaptive quantization or source-adaptive modulation.Although Information Theory is not the main topic of the book — in fact, the concept of joint source-channel coding is contradictory to the classical system design motivated by a questionable practical interpretation of the separation theorem — this theory still provides the ultimate performance limits for any practical system, whether it uses joint source-channel coding or not. Therefore, the theoretical limits are presented in a self-contained appendix, which is a useful reference also for those not directly interested in the main topic of this book./a

Channel Optimized Vector Quantization
Channel Optimized Vector Quantization

Author: Hamidreza Ebrahimzadeh Saffar

Publisher:

Published: 2008

Total Pages: 234

ISBN-13:

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Joint source-channel coding (JSCC) has emerged to be a major field of research recently. Channel optimized vector quantization (COVQ) is a simple feasible JSCC scheme introduced for communication over practical channels. In this work, we propose an iterative design algorithm, referred to as the iterative maximum a posteriori (MAP) decoded (IMD) algorithm, to improve COVQ systems. Based on this algorithm, we design a COVQ based on symbol MAP hard-decision demodulation that exploits the non-uniformity of the quantization indices probability distribution. The IMD design algorithm consists of a loop which starts by designing a COVQ, obtaining the index source distribution, updating the discrete memoryless channel (DMC) according to the achieved index distribution, and redesigning the COVQ. This loop stops when the point-to-point distortion is minimized. We consider memoryless Gaussian and Gauss-Markov sources transmitted over binary phase-shift keying modulated additive white Gaussian noise (AWGN) and Rayleigh fading channels. Our scheme, which is shown to have less encoding complexity than conventional COVQ and less encoding complexity and storage requirements than soft-decision demodulated (SDD) COVQ systems, is also shown to provide a notable signal-to-distortion ratio (SDR) gain over the conventional COVQ designed for hard-decision demodulated channels while sometimes matching or exceeding the SDD COVQ performance, especially for higher quantization dimensions and/or rates. In addition to our main result, we also propose another iterative algorithm to design SDD COVQ based on the notion of the JSCC error exponent. This system is shown to have some gain over classical SDD COVQ both in terms of the SDR and the exponent itself.

Optimal Multiresolution Quantization for Broadcast Channels with Random Index Assignment
Optimal Multiresolution Quantization for Broadcast Channels with Random Index Assignment

Author: Fei Teng

Publisher:

Published: 2010

Total Pages: 56

ISBN-13:

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Shannon's classical separation result holds only in the limit of infinite source code dimension and infinite channel code block length. In addition, Shannon theory does not address the design of good source codes when the probability of channel error is nonzero, which is inevitable for finite-length channel codes. Thus, for practical systems, a joint source and channel code design could improve performance for finite dimension source code and finite block length channel code, as well as complexity and delay. Consider a multicast system over a broadcast channel, where different end users typically have different capacities. To support such user or capacity diversity, it is desirable to encode the source to be broadcasted into a scalable bit stream along which multiple resolutions of the source can be reconstructed progressively from left to right. Such source coding technique is called multiresolution source coding. In wireless communications, joint source channel coding (JSCC) has attracted wide attention due to its adaptivity to time-varying channels. However, there are few works on joint source channel coding for network multicast, especially for the optimal source coding over broadcast channels. In this work, we aim at designing and analyzing the optimal multiresolution vector quantization (MRVQ) in conjunction with the subsequent broadcast channel over which the coded scalable bit stream would be transmitted. By adopting random index assignment (RIA) to link MRVQ for the source with superposition coding for the broadcast channel, we establish a closed-form formula of end-to-end distortion for a tandem system of MRVQ and a broadcast channel. From this formula we analyze the intrinsic structure of end-to-end distortion (EED) in a communication system and derive two necessary conditions for optimal multiresolution vector quantization over broadcast channels with random index assignment. According to the two necessary conditions, we propose a greedy iterative algorithm for jointly designed MRVQ with channel conditions, which depends on the channel only through several types of average channel error probabilities rather than the complete knowledge of the channel.

Source and Channel Coding
Source and Channel Coding

Author: John B. Anderson

Publisher: Springer Science & Business Media

Published: 1991-09-30

Total Pages: 452

ISBN-13: 9780792392101

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oW should coded communication be approached? Is it about prob H ability theorems and bounds, or about algorithms and structures? The traditional course in information theory and coding teaches these together in one course in which the Shannon theory, a probabilistic the ory of information, dominates. The theory's predictions and bounds to performance are valuable to the coding engineer, but coding today is mostly about structures and algorithms and their size, speed and error performance. While coding has a theoretical basis, it has a practical side as well, an engineering side in which costs and benefits matter. It is safe to say that most of the recent advances in information theory and coding are in the engineering of coding. These thoughts motivate the present text book: A coded communication book based on methods and algorithms, with information theory in a necessary but supporting role. There has been muchrecent progress in coding, both inthe theory and the practice, and these pages report many new advances. Chapter 2 cov ers traditional source coding, but also the coding ofreal one-dimensional sources like speech and new techniques like vector quantization. Chapter 4 is a unified treatment of trellis codes, beginning with binary convolu tional codes and passing to the new trellis modulation codes.

Joint Source-Channel Decoding
Joint Source-Channel Decoding

Author: Pierre Duhamel

Publisher: Academic Press

Published: 2010-01-07

Total Pages: 344

ISBN-13:

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Gives the tools to develop applications in video broadcasting with the improved quality of service offered by joint-source channel decoding.