Guogang Hua, Lei Cao, Chang Wen Chen · 2007
Distributed source coding (DSC) has been attracting more and more research interests recently, particularly for mobile wireless applications. However, most existing DSC schemes assume that the coded information is transmitted error free to the decoder end. In this paper, we report our latest investigations on distributed source coding under noisy transmission environments. It is expected that these investigations provide useful guidelines for practical DSC system design when the transmission is actually not error free. Especially, many current schemes in DSC adopted the channel coding principles to accomplish the source coding tasks. We shall illustrate that the channel coding-based DSC schemes possess some inherent noise combating capability to recover possible channel errors under noisy transmission environments. Specifically, we shall report the investigations of Turbo DSC under various environments: error free, noisy transmission with additional forward error correction (FEC) and noisy transmission without FEC. Our experimental results show that, with the same number of bits transmitted through noise channel, the unprotected Turbo DSC outperforms the FEC protected Turbo DSC.
Cited by 4
Κολομβάκης Νικόλαος · Technical University of Crete · 2014
Μη διαθέσιμο
Cited by 0Open access
Nan Ma, Prakash Ishwar · ArXiv.org · 2008
A two-terminal interactive distributed source coding problem with alternating messages for function computation at both locations is studied. For any number of messages, a computable characterization of the rate region is provided in terms of single-letter information measures. While interaction is useless in terms of the minimum sum-rate for lossless source reproduction at one or both locations, the gains can be arbitrarily large for function computation even when the sources are independent. For a class of sources and functions, interaction is shown to be useless, even with infinite messages, when a function has to be computed at only one location, but is shown to be useful, if functions have to be computed at both locations. For computing the Boolean AND function of two independent Bernoulli sources at both locations, an achievable infinite-message sum-rate with infinitesimal-rate messages is derived in terms of a two-dimensional definite integral and a rate-allocation curve. A general framework for multiterminal interactive function computation based on an information exchange protocol which successively switches among different distributed source coding configurations is developed. For networks with a star topology, multiple rounds of interactive coding is shown to decrease the scaling law of the total network rate by an order of magnitude as the network grows.
Cited by 12Open access
Abdulah Jeza Aljohani, Soon Xin Ng · IEEE Access · 2020
Distributed Source Coding (DSC) schemes rely on separate encoding but joint decoding of statistically dependent sources, which exhibit correlation. More specifically, Distributed Joint Source-Channel coding (DJSC) is associated with the scenario, where the correlated source signals are transmitted through a noisy channel. On one hand, employing DSC or DJSC schemes exploits the existing correlation between sensors resulting in minimising the transmission energy required by the sources, while maintaining reliable communication. On the other hand, Network Coding (NC) is an efficient data transport technique leveraging network efficiency, by allowing Relay Nodes (RNs) in a communication network to combine multiple data packets received via the incoming links before transmitting them to the Destination Node (DN). In this paper, the bandwidth-efficient Distributed Joint Turbo Trellis-Coded Modulation (DJTTCM) aided by both Dynamic Network Coding (DNC) and Adaptive DNC (ADNC)-based cooperative transmission schemes are proposed. Both systems are proposed for supporting correlated source transmissions over hostile channels experiencing both small-scale and large-scale fading in which the RNs dynamically transmit its non-binary linear combinations to the DN. A substantial gain of 19.5 dB was attained at a correlation coefficient of ρ = 0.8 over its counterpart dispensing with NC.
Cited by 5Open access
Mortuza Ali, Manzur Murshed · 2006
Information theoretic proof exists to support that independent encoding of distributed sources with a joint decoder by exploiting the correlation among the sources can be as efficient as encoding them jointly. This paper successfully attempts to apply this concept for lossless video coding using lattices to divide the multidimensional integer pixel intensity hyperspace of a block of pixels into a finite number of cosets and encoding each block with its coset index. This radical departure from conventional predictive coding techniques not only offers very low computational complexity by avoiding expensive learning of predictor coefficients but also avoids any coding loss due to rounding of real-valued predictions. On standard test video sequences, this scheme achieved compression within as low as 4.6% of the latest scheme with optimal learning of predictor coefficients. The former, however, outperformed the latter as soon as it started updating the optimal predictor coefficients less frequently to reduce the computational complexity.
Cited by 2
Soon Xin Ng, Kai Zhu, Lajos Hanzo · 2010
In this contribution, we propose a novel Distributed Source-coding, Channel-coding and Modulation (DSCM) scheme for cooperative communications. The proposed DSCM scheme is designed for ensuring the decoding convergence of its constituent component codes, namely, the Variable Length Code (VLC) and two Coded Modulation (CM) schemes. The source node encodes the source symbols with the aid of a serially-concatenated VLC and a Turbo Trellis Coded Modulation (TTCM) scheme. The TTCM-VLC encoded symbols are transmitted to both a relay node and to the destination node during the first transmission period. The relay node employs a powerful iterative TTCM-VLC decoder for estimating the VLC-encoded bit sequence. This decoded bit sequence is then re-encoded with the aid of a simple Trellis Coded Modulation (TCM) scheme before it is transmitted to the destination node. At the destination node, a novel four-component iterative decoding arrangement is invoked for recovering the original source symbols. It is shown that the DSCM scheme significantly outperforms the TTCM-VLC benchmarker scheme dispensing with relaying. The proposed power- and bandwidth-efficient DSCM scheme is an ideal candidate for next-generation mobile multimedia systems.
Cited by 11
Takayuki Nakachi · 2011
Distributed Source Coding (DSC) implements inter source redundancy by encoding correlated information sources, without letting the sources communicate with each other. The DSC theorem states that the optimal rate achieved by joint encoding and decoding can be reached by separate encoding and joint decoding. Video coding based on the DCS theorem is Distributed Video Coding (DVC). DVC is attracting attention as a new paradigm for video compression. While conventional video coding schemes such as H.264/AVC reduce inter-frame redundancy at the encoder, DVC reduces inter-frame redundancy at the decoder by using distributed channel coding. The architecture suits applications that require low-complexity encoders. Examples of such applications include wireless video surveillance and mobile camera phones. In this paper, after describing the DSC theorem, we offer a survey of recent trends and problems in DVC and then introduce our contributions.
Cited by 1
Jun Muramatsu · 2025
This paper investigates distributed source coding, multiple description coding, and source coding with side information at decoders. The equivalence between the multiple-decoder extension of distributed source coding with side information at decoders and the multiple-source extension of multiple description coding with side information at decoders is clarified. Their multi-letter rate-distortion region is characterized by entropy functions for arbitrary general correlated sources.
Cited by 0
Tao Cui, Tracey Ho, Lijun Chen · 2007
In this paper, we consider minimum cost lossless source coding for multiple multicast sessions. Each session comprises a set of correlated sources whose information is demanded by a set of sink nodes. We propose a distributed end-to-end algorithm which operates over given multicast trees, and a back-pressure algorithm which optimizes routing and coding over the whole network. Unlike other existing algorithms, the source rates need not be centrally coordinated; the sinks control transmission rates across the sources. With random network coding, the proposed approach yields completely distributed and optimal algorithms for intra-session network coding. We prove the convergence of our proposed algorithms. Some practical considerations are also discussed. Experimental results are provided to complement our theoretical analysis.
Cited by 6
Marco Tagliasacchi, Stefano Tubaro · 2005
Motion Compensated Temporal Filtering (MCTF) has proved to be an efficient coding tool in the design of open-loop scalable video codecs. In this paper we propose a MCTF video coding scheme based on lifting where the prediction step is implemented using PRISM (Power efficient, Robust, hIgh compression Syndrome-based Multimedia coding), a video coding framework built on distributed source coding principles. We study the effect of integrating the update step at the encoder or at the decoder side. We show that the latter approach allows to improve the quality of the side information exploited during decoding. We present the analytical results obtained by modeling the video signal along the motion trajectories as a first order auto-regressive process. We show that the update step at the decoder allows to half the contribution of the quantization noise. We also include experimental results with real video data that demonstrate the potential of this approach when the video sequences are coded at low bitrates.
Cited by 26
Christine Guillemot, Aline Roumy, V. Toto-Zarasoa · 2010
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Stark C. Draper, Ashish Khisti, Emin Martinian et al. · 2007
We describe a method to encode fingerprint biometrics securely for use, e.g., in encryption or access control. The system is secure because the stored data does not suffice to recreate the original fingerprint biometric. Therefore, a breach in database security does not lead to the loss of biometric data. At the same time the stored data suffices to validate a probe fingerprint. Our approach is based on the use of distributed source coding techniques implemented with graph-based codes. We present a statistical model of the relationship between the enrollment biometric and the (noisy) biometric measurement taking during authentication. We describe how to validate or reject a candidate biometric probe given the probe and the stored encoded data. We report the effectiveness of our method as tested on a database consisting of 579 data sets, each containing roughly 15 measurements of a single finger. We thereby demonstrate a working secure biometric system for fingerprints.
Cited by 98
Jiancheng Tang, Qianqian Yang · Entropy · 2025
Motivated by task-oriented semantic communication and distributed learning systems, this paper studies a distributed indirect source coding problem where <i>M</i> correlated sources are independently encoded for a central decoder. The decoder has access to correlated side information in addition to the messages received from the encoders and aims to recover a latent random variable under a given distortion constraint rather than recovering the sources themselves. We characterize the exact rate-distortion function for the case where the sources are conditionally independent given the side information. Furthermore, we develop a distributed Blahut-Arimoto (BA) algorithm to numerically compute the rate-distortion function. Numerical examples are provided to demonstrate the effectiveness of the proposed approach in calculating the rate-distortion region.
Cited by 1Open access
Guang, Xuan, Sun, Xiufang, Zhang, Ruze · ArXiv.org · 2025
Inspired by mobile satellite communication systems and the important and prevalent applications of computational tasks, we consider a distributed source coding model for compressing vector-linear functions, which consists of multiple sources, multiple encoders and a decoder linked to all the encoders. Each encoder has access to a certain subset of the sources and the decoder is required to compute with zero error a vector-linear function of the source information, which corresponds to a matrix $T$. The connectivity state between the sources and the encoders and the vector-linear function are all arbitrary. In the paper, we are interested in the function-compression capacity to measure the efficiency of using the system. We first present a general lower bound on the function-compression capacity applicable to arbitrary connectivity states and vector-linear functions. Next, we confine to the nontrivial models with only three sources and no more than three encoders, and prove that all the $3\times2$ column-full-rank matrices $T$ can be divided into two types $T_1$ and $T_2$, for which the function-compression capacities are identical if the matrices $T$ have the same type. We explicitly characterize the function-compression capacities for two most nontrivial models associated with $T_2$ by a novel approach of both upper bounding and lower bounding the size of image sets of encoding functions. This shows that the lower bound thus obtained is not always tight. Rather, by completely characterizing their capacities, the lower bound is tight for all the models associated with $T_1$ and all the models associated with $T_2$ except for the two most nontrivial models. We finally apply the obtained results to network function computation and answer the open problem whether the best known upper bound proved by Guang et. al. (2019) on computing capacity is in general asymptotically tight.
Cited by 0Open access
Vaezi, Mojtaba · eScholarship@McGill (McGill) · 2014
Distributed source coding, separate encoding (compression) and joint decoding of statistically dependent sources, arises in an increasing number of applications like sensor networks and multiview video coding.Many of those applications are highly interactive, requiring the development of low-delay, energy-limited communication and computing schemes.Currently, this compression is performed by using capacity-approaching binary channel codes.As a natural extension, distributed lossy source coding is realized by cascading a quantizer and Slepian-Wolf coding in the binary domain.Despite big strides in practical distributed source coding techniques, this problem is still demanding in terms of processing power, bandwidth, and delay.In this dissertation, we develop a new framework for distributed lossy source coding, in which we use real-number codes for binning.Specifically, we use a class of Bose-Chaudhuri-Hocquenghem (BCH) codes in the real/complex field known as the discrete Fourier transform (DFT) codes.Contrary to the conventional scheme, we first compress the continuousvalued sources and then quantize them.The new scheme exploits the correlation between continuous-valued sources, rather than quantized ones, which is more accurate.Also, by using short BCH-DFT codes, it reduces the complexity and delay and offers the potential to avoid the problems of the conventional quantization and binning approach, with relatively simple encoder/decoder.We propose both syndrome-and parity-based schemes, and we extend the parity-based scheme to distributed joint source-channel coding based on a single DFT code.Further, to adapt to uncertainty in the degree of statistical dependence between the sources, we construct rate-adaptive BCH-DFT codes.This allows the encoder to switch flexibly between encoding sample rates, if the degree of statistical dependence varies.The construction of rate-adaptive codes is based on transmission of additional syndrome samples and a simple extension of the subspace-based decoding.Another major contribution of this dissertation is to generalize the encoding/decoding of BCH-DFT codes.We prove that the parity frequencies of a BCH-DFT code, or equivalently the zeros of codewords in the frequency domain, are not required to be adjacent; we provide the decoding algorithm as well.This offers flexibility in constructing BCH-DFT codes and further improvement in the decoding which can be exploited in channel coding as well.Doctoral Award, NSERC and Hydro-Qubec throughout my Ph.D. study.All my colleagues in the Telecommunications and Signal Processing group were sources of inspiration and friendship.I wish to give a special thanks to Sina for so many stimulating discussions, but
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Somayeh Salimi, Mahmoud Salmasizadeh, Mohammad Reza Aref · IET Communications · 2010
New inner and outer bounds on the achievable compression–equivocation rate region for generalised secure data compression with side information are given that do not match in general. In this setup, two senders, Alice and Charlie intend to transmit information to Bob via channels with limited capacity so that he can reliably reconstruct their observations. The eavesdropper, Eve, has access to one of the channels at each instant and is interested in the source of the same channel at the time. Bob and Eve also have their own observations, which are correlated with Alice's and Charlie's observations. In this model, two equivocation and compression rates are defined with respect to the sources of Alice and Charlie. Furthermore, different special cases are discussed where the inner and outer bounds match. Our model covers the previously obtained results as well.
Cited by 6
Michael Gastpar · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 2004
In this paper, we investigate properties of good coding strategies for a class of wireless sensor networks that could be termed "monitoring" networks: their task is to monitor an underlying physical reality at the highest possible fidelity. Since the sensed signals are often analog, and the communication channels noisy, it is not generally possible to exactly communicate the sensed signals. Rather, such sensor network scenarios involve both a compression and a communication problem. It is well known that these two tasks must be addressed jointly for optimal performance, but optimal performance is unknown in general. This problem is addressed from a scaling-law perspective in this paper, i.e., as the number of nodes becomes large. The goal of the paper is to characterize the key properties of coding strategies that achieve the optimum scaling behavior, and hence to identify the scaling-law relevant issues in code design. We first present a lower bound to the cost-distortion tradeoff, and then compare two fundamentally different coding strategies to that lower bound.
Cited by 2Open access
Pradeepa Yahampath · 2007
This paper investigates the design of a system of predictive vector quantizers for distributed sources with memory, in which linear prediction is used to exploit the source memory, while distributed quantization is used to exploit the correlation between sources. A training-based algorithm is proposed for jointly designing the predictors, binning functions, and reconstruction codebooks of the given system to match the intra-and inter-source correlations. In order to demonstrate the effectiveness of the algorithm, experimental results obtained by designing both scalar and vector quantizers for a set of distributed Gauss-Markov sources are presented. While the optimality of these designs is unknown, it is shown that they convincingly outperform several other alternatives.
Cited by 10
Yasutada Oohama · IEEE Transactions on Information Theory · 2012
We consider the distributed source coding system for <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$L$</tex></formula> correlated memoryless Gaussian remote sources specified with <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$L$</tex></formula> correlated Gaussian random variables <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$X_{i}$</tex></formula> , <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$i=1,2,\ldots,L$</tex></formula> . We deal with the case where each of <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$L$</tex></formula> distributed encoders cannot directly observe the source <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$X_{i}$</tex></formula> but its noisy version <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$Y_{i}=X_{i}+N_{i}$</tex></formula> . Here, <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$N_{i}$</tex> </formula> , <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$i=1,2,\ldots, L$</tex></formula> are independent additive <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$L$</tex></formula> Gaussian noises also independent of <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$X_{i}$</tex> </formula> , <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$i=1,2,\ldots, L$</tex></formula> . On this coding system, the determination problem of the rate distortion region remains open in general. In this paper, we derive explicit outer and inner bounds of the rate distortion region. We further find an explicit sufficient condition for those two bounds to match. We also study the sum rate part of the rate distortion region when the correlation has some symmetrical property. We derive an explicit upper bound of the sum rate part from the inner bounds of the rate distortion region. On a lower bound, we derive a new explicit bound by making full use of the symmetrical property of the correlation. We further derive an explicit necessary and sufficient condition for the lower bound to coincide with the upper bound.
Cited by 32
Teratani, Mehrdad, Droese, Michael, Fujii, Toshiaki et al. · Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) · 2004
info:eu-repo/semantics/published
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