This correspondence provides bounds on the rate-distortion region for the distributed compression scenario where two (or more) sources are compressed separately for a decoder that has access to side information. Concl...
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This correspondence provides bounds on the rate-distortion region for the distributed compression scenario where two (or more) sources are compressed separately for a decoder that has access to side information. Conclusive rate-distortion results are found for the case where the sources are conditionally independent, given the side information.
We improve the overall rate-distortion performance of distributed video coding by efficient techniques of correlation noise estimation and key frame encoding. In existing transform-domain Wyner-Ziv video coding method...
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We improve the overall rate-distortion performance of distributed video coding by efficient techniques of correlation noise estimation and key frame encoding. In existing transform-domain Wyner-Ziv video coding methods, blocks within a frame are treated uniformly to estimate the correlation noise even though the success of generating side information is different for each block. We propose a method to estimate the correlation noise by differentiating blocks within a frame based on the accuracy of the side information. Simulation results show up to 2 dB improvement over conventional methods without increasing encoder complexity. Also, in traditional Wyner-Ziv video coding, the intercorrelation of key frames is not exploited since they are simply intracoded. In this paper, we propose a frequency band coding mode selection for key frames to exploit similarities between adjacent key frames at the decoder. Simulation results show significant improvement especially for low-motion and high frame rate sequences. Furthermore, the advantage of applying both schemes in a hierarchical order is investigated. This method achieves additional improvement.
The reconstruction of a bounded deterministic field from binary-quantized observations of sensors which are randomly deployed over the field domain is studied. The sensor observations are corrupted by bounded additive...
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The reconstruction of a bounded deterministic field from binary-quantized observations of sensors which are randomly deployed over the field domain is studied. The sensor observations are corrupted by bounded additive noise. The study focuses on the extremes of lack of deterministic control in the sensor deployment, lack of knowledge of the noise distribution, and lack of sensing precision and reliability. Such adverse conditions are motivated by possible real-world scenarios where a large collection of low-cost, crudely manufactured sensors are mass-deployed in an environment where little can be assumed about the ambient noise. A simple estimator that reconstructs the entire field from these unreliable, binary-quantized, noisy observations is proposed. Technical conditions for the almost sure and mean squared error (MSE) convergence of the estimate to the field, as the number of sensors tends to infinity, are derived and their implications are discussed. For finite-dimensional, bounded-variation, and Sobolev-differentiable function classes, specific MSE decay rates are derived.
It is well known that distributed source coding can improve the reliability and energy efficiency of wireless sensor networks by exploiting the correlation among sensing data. In this letter, we strengthen the benefit...
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It is well known that distributed source coding can improve the reliability and energy efficiency of wireless sensor networks by exploiting the correlation among sensing data. In this letter, we strengthen the benefits of such an approach by proposing a simple, general, efficient power allocation scheme for an arbitrary amount of sensors. The proposed power allocation is asymptotically optimal at high signal-to-noise ratio.
In this letter, we consider binary low density parity check accumulate (LDPCA) codes with source revealing rate-adaptation for distributed source coding (DSC) applications. We propose an effective algorithm for select...
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In this letter, we consider binary low density parity check accumulate (LDPCA) codes with source revealing rate-adaptation for distributed source coding (DSC) applications. We propose an effective algorithm for selecting source symbols to the source revealing. Numerical results demonstrate that the proposed scheme significantly outperforms the conventional LDPCA codes in the high compression rate region in asymmetric DSC systems.
We consider the problem of compression of two memoryless binary sources, the correlation between which is defined by a Hidden Markov Model (HMM). We propose a Decision Feedback (DF) based scheme which when used with l...
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We consider the problem of compression of two memoryless binary sources, the correlation between which is defined by a Hidden Markov Model (HMM). We propose a Decision Feedback (DF) based scheme which when used with low density parity check codes results in compression close to the Slepian-Wolf limit.
Minimum mean square error (MMSE) decoding in a large-scale sensor network which employs distributed quantization is considered. Given that the computational complexity of the optimal decoder is exponential in the netw...
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Minimum mean square error (MMSE) decoding in a large-scale sensor network which employs distributed quantization is considered. Given that the computational complexity of the optimal decoder is exponential in the network size, we present a framework based on Bayesian networks for designing a near-optimal decoder whose complexity is only linear in network size (hence scalable). In this approach, a complexity-constrained factor graph, which approximately represents the prior joint distribution of the sensor outputs, is obtained by constructing an equivalent Bayesian network using the maximum likelihood (ML) criterion. The decoder executes the sum-product algorithm on the simplified factor graph. Our simulation results have shown that the scalable decoders constructed using the proposed approach perform close to optimal, with both Gaussian and non-Gaussian sensor data.
作者：
Fang, YongNW A&F Univ
Coll Informat Engn Shaanxi Yangling 712100 Peoples R China
This paper reveals that the residual redundancies in LDPC syndromes can be used to estimate the crossover probability between two correlated binary sequences. This work simplifies the algorithm proposed in [10]. In ad...
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This paper reveals that the residual redundancies in LDPC syndromes can be used to estimate the crossover probability between two correlated binary sequences. This work simplifies the algorithm proposed in [10]. In addition, by using the mean of intrinsic Log-Likelihood Ratios (LLRs) of LDPC syndromes, this work also applies to irregular LDPC codes. The proposed algorithm performs well in simulations.
Joint source-channel coding of multiple correlated sources transmitted over orthogonal channels is considered. Two optimization problems related to coding schemes are formulated: maximal common rate, and maximal sum r...
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Joint source-channel coding of multiple correlated sources transmitted over orthogonal channels is considered. Two optimization problems related to coding schemes are formulated: maximal common rate, and maximal sum rate. These two problems model different power saving situations in energy-constrained wireless sensor networks. Solutions of these problems for an arbitrary number of sources are described, and some specific examples are given.
We propose a new technique, compress-spread-forward (CSF), for high-performance wireless streaming from two base stations in parallel. CSF uses multiterminal sourcecoding for efficient source compression and complete...
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We propose a new technique, compress-spread-forward (CSF), for high-performance wireless streaming from two base stations in parallel. CSF uses multiterminal sourcecoding for efficient source compression and complete complementary sequences for error-free multiple access and synchronization. Our practical design shows significant performance gains due to spatial diversity and distributed source coding.
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