The problem of error control and concealment in video communication is becoming increasingly important because of the growing interest in video delivery over unreliable channels such as wireless networks and the Internet. This paper reviews the techniques that have been developed for error control and concealment in the past ten to fifteen years. These techniques are described in three categories according to the roles that the encoder and decoder play in the underlying approaches. Forward error concealment includes methods that add redundancy at the source end to enhance error resilience of the coded bit streams. Error concealment by postprocessing refers to operations at the decoder to recover the damaged areas based on characteristics of image and video signals. Finally, interactive error concealment covers techniques that are dependent on a dialog between the source and destination. Both current research activities and practice in international standards are covered.

Abstract—We analyze the dependencies between the variables involved in the source and channel coding chain. This analysis is carried out in the framework of Bayesian networks, which provide both an intuitive representation for the global model of the coding chain and a way of deriving joint (soft) decoding algorithms. Three sources of dependencies are involved in the chain: 1) the source model, a Markov chain of symbols; 2) the source coder model, based on a variable length code (VLC), for example a Huffman code; and 3) the channel coder, based on a convolutional error correcting code. Joint decoding relying on the hidden Markov model (HMM) of the global coding chain is intractable, except in trivial cases. We advocate instead an iterative procedure inspired from serial turbo codes, in which the three models of the coding chain are used alternately. This idea of using separately each factor of a big product model inside an iterative procedure usually requires the presence of an interleaver between successive components. We show that only one interleaver is necessary here, placed between the source coder and the channel coder. The decoding scheme we propose can be viewed as a turbo algorithm using alternately the intersymbol correlation due to the Markov source and the redundancy introduced by the channel code. The intermediary element, the source coder model, is used as a translator of soft information from the bit clock to the symbol clock. Index Terms—Bayesian network, data compression, entropy coding, iterative decoding, joint source-channel decoding, probabilistic inference, soft decoding, turbo code, variable length code. I.

Multimedia transmission has to handle a variety of compressed and uncompressed source

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Exploiting the residual redundancy in a source coder output stream during the decoding process has been proven to be a bandwidth efficient way to combat the noisy channel degradations. In this paper, we consider soft reconstruction of LSF parameters in IS641 CELP coder transmitted over a noisy channel. We propose two schemes. The first scheme attempts to exploit the interframe residual redundancies in the sequence of received parameters. The second approach exploits both interframe and intraframe residual redundancies. Simulation results are provided which demonstrates the efficiency of the algorithms. Another issue addressed here, is a methodology to efficiently approximate and store the residual redundancies or the a priori transition probabilities. For quantizers with high rates calculating these probabilities require a huge number of source samples. As well, storing them require a large amount of memory. These issues can well make the decoder design process an impractical task. The proposed method is based on the classification of the signal domain. The presented schemes provide high quality error concealment solutions for CELP coders.

Shannon's source-channel separation theorem holds only under asymptotic conditions, where both source and channel codes are allowed infinite length and complexity, which is not possible in practice. This observation has led to the increasing popularity of joint source-channel encoding and decoding schemes as viable alternatives for achieving reliable communication of signals across noisy channels. Joint source-channel encoders (JSCE) aim at designing the source and channel encoder of a system in some joint sense, while joint source-channel decoders (JSCD) concentrate on the design of the decoder. JSCD schemes have been

We propose a macroscopic multistage compression system to provide progressive and robust transmission of images across noisy channels with varying statistics. Each stage encodes the residual image of the previous stage. The choice of source coder and transmission rate at each stage are design parameters. The multistage structure allows the use of efficient unequal error protection channel coding and introduces source redundancy to enable graceful degradation under severe channel conditions. Both bit errors and packet losses are considered. Specific examples are provided to demonstrate the performance of the proposed method. Keywords: robust image compression, packet erasure, multistage coding 1. INTRODUCTION Modern communication systems experience channel conditions that change over time due to such things as mobile receivers and transmitters or varying levels of multiuser interference. Handheld wireless devices, for example, must operate in severe channel conditions with limited ban...

This document is intended to be tutorial in nature. No prior knowledge of image processing concepts is ECE 802 – 602: Information Theory and Coding Seminar 1 – The Discrete Cosine Transform: Theory and Application Transform coding constitutes an integral component of contemporary image/video processing applications. Transform coding relies on the premise that pixels in an image exhibit a certain

We describe a new way to organize a full search vector quantization codebook so that images encoded with it can be sent progressively and have resilience to channel noise. The codebook organization guarantees that the most significant bits (MSB's) of the codeword index are most important to the overall image quality and are highly correlated. Simulations show that the effective channel error rates of the MSB's can be substantially lowered by implementing a Maximum A Posteriori (MAP) detector similar to one suggested by Phamdo and Farvardin [12]. The performance of the scheme is close to that of Pseudo-Gray [22] coding at lower bit error rates and outperforms it at higher error rates. No extra bits are used for channel error correction.

Abstract — An approach to optimal soft decoding for vector quantization (VQ) over a code-division multiple-access (CDMA) channel is presented. The decoder of the system is soft in the sense that the unquantized outputs of the matched filters are utilized directly for decoding (no decisions are taken), and optimal according to the minimum mean-squared error (MMSE) criterion. The derived decoder utilizes a priori source information and knowledge of the channel characteristics to combat channel noise and multiuser interference in an optimal fashion. Hadamard transform representations for the user VQ’s are employed in the derivation and for the implementation of the decoder. The advantages of this approach are emphasized. Suboptimal versions of the optimal decoder are also considered. Simulations show the soft decoders to outperform decoding based on maximum-likelihood (ML) multiuser detection. Furthermore, the suboptimal versions are demonstrated to perform close to the optimal, at a significantly lower complexity in the number of users. The introduced decoders are, moreover, shown to exhibit near–far resistance. Simulations also demonstrate that combined source–channel encoding, with joint source–channel and multiuser decoding, can significantly outperform a tandem source–channel coding scheme employing multiuser detection plus table lookup source decoding. Index Terms—Code-division multiple access, combined source and channel coding, joint optimization, multiuser detection, multiuser systems, soft decoding, vector quantization. I.