Multiterminal source coding for correlated memoryless Gaussian sources with several side information at the decoder

Oohama, Yasutada

doi:10.1109/itcom.1999.781431

Cited by 21 publications

(8 citation statements)

References 2 publications

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“…This problem, which is also called the 1-helper problem, for the special case of correlated memoryless Gaussian sources and squared distortion measures is investigated by Oohama [17]. In the m-helper problem only the main sources is reconstructed while other m sources work as helpers [18][19][20][21].…”

Section: One-helper Coding Schemementioning

confidence: 99%

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Successively Structured Gaussian Two-terminal Source Coding

Behroozi

Soleymani

2008

Wireless Pers Commun

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Multiterminal source coding refers to separate encoding and joint decoding of multiple correlated sources. Joint decoding requires all the messages to be decoded simultaneously which is exponentially more complex than a sequence of single-message decodings. Inspired by previous work on successive coding, we apply the successive Wyner-Ziv coding, which is inherently a low complexity approach of obtaining a prescribed distortion, to the two-terminal source coding scheme. First, we consider 1-helper problem where one source provides partial side information to the decoder to help the reconstruction of the main source. Our results show that the successive coding strategy is an optimal strategy in the sense of achieving the rate-distortion function. By developing connections between source encoding and data fusion steps, it is shown that the whole rate-distortion region for the 2-terminal source coding problem is achievable using the successive coding strategy. Comparing the performance of the sequential coding with the performance of the successive coding, we show that there is no sum-rate loss when the side information is not available at the encoder. This result is of special interest in some applications such as video coding where there are processing and storage constraints at the encoder. Finally, we provide an achievable rate-distortion region for the m-terminal source coding.

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Section: One-helper Coding Schemementioning

confidence: 99%

“…The special case of Gaussian source and noise statistics with a MSE distortion is called the quadratic Gaussian CEO problem [31]. For this case, the rate region is known [19,20,32,33]. Applications of the successive coding strategy in the CEO problem are presented in [8,34,35].…”

Section: Ceo Problemmentioning

confidence: 99%

Successively Structured Gaussian Two-terminal Source Coding

Behroozi

Soleymani

2008

Wireless Pers Commun

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“…Theoretical limit of MT source coding of jointly Gaussian sources was given recently in [2] for the direct setting (with two encoders) where the encoders directly observe the sources, and in [3], [4] for the indirect/CEO setting where the encoders observe independently corrupted versions of the same source. Practical MT code designs based on generalized coset codes were provided by Pradhan and Ramchandran in [5].…”

Section: Introductionmentioning

confidence: 99%

Multiterminal Video Coding

Yang

Stankovic

Zhao

et al. 2007

2007 IEEE International Conference on Image Processing

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Abstract-Following recent works on the rate region of the quadratic Gaussian two-terminal source coding problem and limit-approaching code designs, this paper examines multiterminal source coding of two correlated video sequences to save the sum rate over independent coding. Specifically, the first video sequence is coded by H.264 and used at the joint decoder to facilitate Wyner-Ziv coding of the second video sequence. An efficient stereo matching algorithm based on loopy belief propagation is then adopted at the decoder to produce pixel-level disparity maps between the corresponding frames of the two decoded video sequences on the fly. Based on the disparity maps, side information for both motion vectors and motion-compensated residual frames of the second sequence are generated at the decoder before Wyner-Ziv encoding. Preliminary results on stereo video sequences using H.264 in conjunction with LDPC codes for Slepian-Wolf coding of the motion vectors show savings in terms of the sum rate when compared to separate coding at the same video quality.

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“…Oohama [7] gave the solution of Wyner-Ziv problem with coded side information for the case of two sources. He extended his results to more than two sources provided that the sources are conditionally independent given one of them [8].…”

Section: Introductionmentioning

confidence: 95%

“…To obtain the optimum rate assignment rule , we take the derivation of with respect to (considering the rates in form of nats) and set the result equal to zero, This is a quadratic equation with respect to . Therefore, the optimal fractional rate allocation factor can be obtained as (8) where Since the second derivative of with respect to is positive, is the value of that gives the minimum of distortion-rate tradeoff. However, the of (8) is not always between 0 and 1.…”

Section: Distortion Sum-rate Tradeoffmentioning

confidence: 99%

Distortion sum-rate performance of successive coding strategy in gaussian wireless sensor networks

Behroozi

Soleymani

IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, 2005.

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In this paper, we investigate the distortion sum-rate performance of the successive coding strategy in the so-called quadratic Gaussian CEO problem. In the CEO problem, the central unit or the CEO desires to obtain an optimal estimate of the source signal. Since the source cannot be observed directly, sensors will be deployed to observe independently corrupted versions of the source. They communicate information about their observations to the CEO through rate constrained noiseless channels without cooperating with each other. We consider a distributed sensor network consisting of two sensors with different noise levels and derive the minimum achievable distortion under a sum-rate constraint using the successive coding strategy of [1]. We also demonstrate that the best way to achieve minimum distortion under a sum-rate constraint is to allocate more rate to the sensor with higher quality of observation in a generalized water-filling manner. The fractional rate allocation is approximately 1/2 if the sum-rate is large. Thus, we can simplify rate allocation problem in a general parallel sensor network with sensors by assigning equal rates to sensors, provided the average rate per sensor node is large. We show that this scheme may not cause a large extra distortion compared with the minimum achievable distortion. Finally, we consider the problem of combining source and channel coding in sensor networks. Two paradigms is considered, Shannon's separation paradigm and joint source-channel coding paradigm. We obtain the distortion-power tradeoffs for both coding paradigms in the Gaussian sensor network with multiple access channel.Index Terms-distortion sum-rate tradeoff, side-information, generalized water-filling, Wyner-Ziv coding, Gaussian sensor networks, CEO problem.

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Multiterminal source coding for correlated memoryless Gaussian sources with several side information at the decoder

Cited by 21 publications

References 2 publications

Successively Structured Gaussian Two-terminal Source Coding

Successively Structured Gaussian Two-terminal Source Coding

Multiterminal Video Coding

Distortion sum-rate performance of successive coding strategy in gaussian wireless sensor networks

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