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.

Source and Channel Coding
Source and Channel Coding

Author: John B. Anderson

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 438

ISBN-13: 1461539986

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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 Coding of Discrete-Time Signals with Continuous Amplitudes
Joint Source-Channel Coding of Discrete-Time Signals with Continuous Amplitudes

Author: Norbert Goertz

Publisher: Imperial College Press

Published: 2007

Total Pages: 207

ISBN-13: 1860948464

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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 OCo 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 OCo 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. Sample Chapter(s). Chapter 1: Introduction (98 KB). Contents: Joint Source-Channel Coding: An Overview; Joint Source-Channel Decoding; Channel-Adaptive Scaled Vector Quantization; Index Assignments for Multiple Descriptions Vector Quantizers; Source-Adaptive Modulation; Source-Adaptive Power Allocation; Appendices: Theoretical Performance Limits; Optimal Decoder for a Given Encoder; Symbol Error Probabilities for M-PSK; Derivative of the Expected Distortion for SAM. Readership: Students at advanced undergraduate and graduate level; practitioners and academics in Electrical and Communications Engineering, Information Technology and Computer Science."

Digital Communications 1
Digital Communications 1

Author: Didier Le Ruyet

Publisher: John Wiley & Sons

Published: 2015-10-02

Total Pages: 392

ISBN-13: 1119232430

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The communication chain is constituted by a source and a recipient, separated by a transmission channel which may represent a portion of cable, an optical fiber, a radio channel, or a satellite link. Whatever the channel, the processing blocks implemented in the communication chain have the same foundation. This book aims to itemize. In this first volume, after having presented the base of the information theory, we will study the source coding techniques with and without loss. Then we analyze the correcting codes for block errors, convutional and concatenated used in current systems.

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.