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56
D.: Distributed video coding
 Proc. of the IEEE 93 (2005) 71–83
"... Distributed coding is a new paradigm for video compression, ..."
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Cited by 207 (10 self)
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Distributed coding is a new paradigm for video compression,
On the duality of Gaussian multipleaccess and broadcast channels
 IEEE Trans. Inf. Theory
, 2004
"... Abstract—We define a duality between Gaussian multipleaccess channels (MACs) and Gaussian broadcast channels (BCs). The dual channels we consider have the same channel gains and the same noise power at all receivers. We show that the capacity region of the BC (both constant and fading) can be writt ..."
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Cited by 70 (13 self)
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Abstract—We define a duality between Gaussian multipleaccess channels (MACs) and Gaussian broadcast channels (BCs). The dual channels we consider have the same channel gains and the same noise power at all receivers. We show that the capacity region of the BC (both constant and fading) can be written in terms of the capacity region of the dual MAC, and vice versa. We can use this result to find the capacity region of the MAC if the capacity region of only the BC is known, and vice versa. For fading channels we show duality under ergodic capacity, but duality also holds for different capacity definitions for fading channels such as outage capacity and minimumrate capacity. Using duality, many results known for only one of the two channels can be extended to the dual channel as well. Index Terms—Broadcast channel (BC), channel capacity, duality, fading channels, multipleinput multipleoutput (MIMO) systems, multipleaccess channel (MAC). I.
DataHiding Codes
 Proc. IEEE
, 2005
"... This tutorial paper reviews the theory and design of codes for hiding or embedding information in signals such as images, video, audio, graphics, and text. Such codes have also been called watermarking codes; they can be used in a variety of applications, including copyright protection for digital m ..."
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Cited by 28 (3 self)
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This tutorial paper reviews the theory and design of codes for hiding or embedding information in signals such as images, video, audio, graphics, and text. Such codes have also been called watermarking codes; they can be used in a variety of applications, including copyright protection for digital media, content authentication, media forensics, data binding, and covert communications. Some of these applications imply the presence of an adversary attempting to disrupt the transmission of information to the receiver; other applications involve a noisy, generally unknown, communication channel. Our focus is on the mathematical models, fundamental principles, and code design techniques that are applicable to data hiding. The approach draws from basic concepts in information theory, coding theory, game theory, and signal processing, and is illustrated with applications to the problem of hiding data in images. Keywords—Coding theory, data hiding, game theory, image processing, information theory, security, signal processing, watermarking. I.
Side information aware coding strategies for sensor networks
 IEEE J. Selected Areas Commun
"... Abstract—We develop coding strategies for estimation under communication constraints in treestructured sensor networks. The strategies have a modular and decentralized architecture. This promotes the flexibility, robustness, and scalability that wireless sensor networks need to operate in uncertain ..."
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Cited by 27 (0 self)
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Abstract—We develop coding strategies for estimation under communication constraints in treestructured sensor networks. The strategies have a modular and decentralized architecture. This promotes the flexibility, robustness, and scalability that wireless sensor networks need to operate in uncertain, changing, and resourceconstrained environments. The strategies are based on a generalization of Wyner–Ziv source coding with decoder side information. We develop solutions for general trees, and illustrate our results in serial (pipeline) and parallel (hubandspoke) networks. Additionally, the strategies can be applied to other network information theory problems. They have a successive coding structure that gives an inherently less complex way to attain a number of prior results, as well as some novel results, for the Chief Executive Officer problem, multiterminal source coding, and certain classes of relay channels. Index Terms—Chief Executive Officer (CEO) problems, data fusion, distributed detection, distributed estimation, multiterminal source coding, rate distortion theory, relay channels, sensor networks, side information, Wyner–Ziv coding. I.
On Joint SourceChannel Coding for the WynerZiv Source and the Gel'fandPinsker Channel
 IEEE Trans. Inform. Theory
, 2002
"... We consider the problem of lossy joint sourcechannel coding in a communication system where the encoder has access to channel state information (CSI) and the decoder has access to side information that is correlated to the source. This configuration combines the WynerZiv model of pure lossy source ..."
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Cited by 22 (3 self)
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We consider the problem of lossy joint sourcechannel coding in a communication system where the encoder has access to channel state information (CSI) and the decoder has access to side information that is correlated to the source. This configuration combines the WynerZiv model of pure lossy source coding with side information at the decoder and the Shannon/Gel'fandPinsker model of pure channel coding with CSI at the encoder. We prove a separation theorem for this communication system, which asserts that there is no loss in asymptotic optimality in applying first, an optimal WynerZiv source code and then, an optimal Gel'fandPinsker channel code. We then derive conditions for the optimality of a symbolbysymbol (scalar) sourcechannel code, and demonstrate situations where these conditions are met. Finally, we discuss a few practical applications, including of overlaid communication where the model under discussion is useful.
Superposition coding for sideinformation channels
 IEEE Trans. Inform. Theory
, 2006
"... We present simple, practical codes designed for the binary and Gaussian dirtypaper channels. We show that the dirty paper decoding problem can be transformed into an equivalent multipleaccess decoding problem, for which we apply superposition coding. Our concept is a generalization of the nested ..."
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Cited by 20 (2 self)
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We present simple, practical codes designed for the binary and Gaussian dirtypaper channels. We show that the dirty paper decoding problem can be transformed into an equivalent multipleaccess decoding problem, for which we apply superposition coding. Our concept is a generalization of the nested lattices approach of Zamir, Shamai and Erez. In a theoretical setting, our constructions are capable of achieving capacity using random component codes and maximumlikelihood decoding. We also present practical implementations of the constructions, and simulation results for both dirtypaper channels. Our results for the Gaussian dirtypaper channel are on par with the best known results for nestedlattices. We discuss the binary dirtytape channel, for which we present a simple, effective coding technique. Finally, we propose a framework for extending our approach to general Gel’fandPinsker channels. Index Terms dirty paper, dirty tape, multipleaccess channel, side information, superposition coding.
Successive refinement for the WynerZiv problem and layered code design
 IEEE Transactions on Signal Processing
, 2004
"... Driven by a host of applications, distributed source coding (DSC) has assumed renewed interest in recent years. WynerZiv coding (WZC), which deals with the ratedistortion problem with side information available only at the decoder, is one case of DSC. In this paper, we focus on successive refineme ..."
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Cited by 20 (4 self)
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Driven by a host of applications, distributed source coding (DSC) has assumed renewed interest in recent years. WynerZiv coding (WZC), which deals with the ratedistortion problem with side information available only at the decoder, is one case of DSC. In this paper, we focus on successive refinement of the WynerZiv problem described in [1]. Similar to the problem in classic source coding, a successive refinement coding scheme for the WynerZiv problem consists of multistage encoders and decoders where each decoder uses all the information generated from previous encoding stages and the side information, which could be different from stage to stage. We call such a scheme successively refinable if the ratedistortion pair associated with any stage falls on the same WynerZiv ratedistortion curve given the corresponding side information. It was shown in [1] that if the side information for all stages are identical, the jointly Gaussian source with squared error distortion measure is successively refinable. We extend successive refinability from jointly Gaussian source to the more general type of sources that the difference between the source and the side information is Gaussian and independent of the side information. As a byproduct, we give an alternative proof that the WynerZiv problem for these sources has no rate loss, where this statement was recently shown in [2]. We then propose a layered (successive) coding scheme using nested scalar quantization and SlepianWolf coding of bit planes based on LDPC codes for the type of sources described above. Moreover, when ideal SlepianWolf coding is assumed, we show that our scheme is practically successively refinable, i.e., there is no performance loss due to layering. For the jointly Gaussian source, our layered coder performs 1.33 to 2.83 dB from the WynerZiv bound for rates ranging from 0.47 to 5.65 bits per sample. The full paper is available at
Distributed Bayesian hypothesis testing in sensor networks
 in Proc. Amer. Control Conf
, 2004
"... Abstract — We consider the scenario of N distributed noisy sensors observing a single event. The sensors are distributed and can only exchange messages through a network. The sensor network is modelled by means of a graph, which captures the connectivity of different sensor nodes in the network. The ..."
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Cited by 19 (1 self)
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Abstract — We consider the scenario of N distributed noisy sensors observing a single event. The sensors are distributed and can only exchange messages through a network. The sensor network is modelled by means of a graph, which captures the connectivity of different sensor nodes in the network. The task is to arrive at a consensus about the event after exchanging such messages. The focus of this paper is twofold: a) characterize conditions for reaching a consensus; b) derive conditions for when the consensus converges to the centralized MAP estimate. The novelty of the paper lies in applying belief propagation as a message passing strategy to solve a distributed hypothesis testing problem for a prespecified network connectivity. We show that the message evolution can be reformulated as the evolution of a linear dynamical system, which is primarily characterized by network connectivity. This leads to a fundamental understanding of as to which network topologies naturally lend themselves to consensus building and conflict avoidance. I.
On the Feasibility of LargeScale Wireless Sensor Networks
 In Proc. 40th Allerton Conf. on Communication, Control and Computing
, 2002
"... We consider the problem of rate/distortion with side information available only at the decoder. For the case of jointlyGaussian source X and side information Y, and meansquared error distortion, Wyner proved in 1976 that the rate/distortion function for this problem is identical to the conditional ..."
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Cited by 16 (2 self)
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We consider the problem of rate/distortion with side information available only at the decoder. For the case of jointlyGaussian source X and side information Y, and meansquared error distortion, Wyner proved in 1976 that the rate/distortion function for this problem is identical to the conditional rate/distortion function RXY, assuming the side information Y is available at the encoder. In this paper we construct a structured class of asymptotically optimal quantizers for this problem: under the assumption of high correlation between source X and side information Y, we show there exist quantizers within our class whose performance comes arbitrarily close to Wyner’s bound. As an application illustrating the relevance of the highcorrelation asymptotics, we also explore the use of these quantizers in the context of a problem of data compression for sensor networks, in a setup involving a large number of devices collecting highly correlated measurements within a confined area. An important feature of our formulation is that, although the pernode throughput of the network tends to zero as network size increases, so does the amount of information generated by each transmitter. This is a situation likely to be encountered often in practice, which allows us to cast under new—and more “optimistic”—light some negative results on the transport capacity of largescale wireless networks. Index terms: Rate/distortion, rate/distortion with side information, quantization, vector quantization, lattice quantization, lattice codes, hexagonal lattice, source coding, network information theory, adhoc networks, sensor networks, multihop radio networks, wireless networks, throughput, capacity.
Information bottleneck for gaussian variables
 in Advances in Neural Information Processing Systems 16
, 2003
"... ∗ Both authors contributed equally The problem of extracting the relevant aspects of data was addressed through the information bottleneck (IB) method, by (soft) clustering one variable while preserving information about another relevance variable. An interesting question addressed in the current ..."
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Cited by 14 (3 self)
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∗ Both authors contributed equally The problem of extracting the relevant aspects of data was addressed through the information bottleneck (IB) method, by (soft) clustering one variable while preserving information about another relevance variable. An interesting question addressed in the current work is the extension of these ideas to obtain continuous representations (embeddings) that preserve relevant information, rather than discrete clusters. We give a formal definition of the general continuous IB problem and obtain an analytic solution for the optimal representation for the important case of multivariate Gaussian variables. The obtained optimal representation is a noisy linear projection to eigenvectors of the normalized correlation matrix Σ xyΣ −1 x, which is also the basis obtained in Canonical Correlation Analysis. However, in Gaussian IB, the compression tradeoff parameter uniquely determines the dimension, as well as the scale of each eigenvector. This introduces a novel interpretation where solutions of different ranks lie on a continuum parametrized by the compression level. Our analysis also provides analytic expression for the optimal tradeoff the information curve in terms of the eigenvalue spectrum. 1