In this paper, we investigate the performances of Gaussian modeling and linear prediction tools for error detection and concealment in the transmission of still images. We consider the transmission of subband encoded ...
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In this paper, we investigate the performances of Gaussian modeling and linear prediction tools for error detection and concealment in the transmission of still images. We consider the transmission of subband encoded images through two types of channels. We model the residual correlation between subband coefficients by considering them as jointly Gaussian variables. The first transmission medium considered is a packet-oriented channel, where some packets are lost during transmission. The problem is to estimate the values of missing coefficients. In this case, particular care must be taken while evaluating correlation matrices from incomplete data. The other system considered is based on a discrete memoryless noisy channel affecting the data being transmitted. The challenge is here first to determine the locations of the errors-which is done through hypotheses tests-and then to replace them by estimates based on their neighbors. The reconstruction via linear prediction is shown to give better results than median filtering based reconstruction. Error detection through this Gaussian model also shows promising results, in particular when channel statistics are taken into account in a jointsource-channel decoding framework.
In this paper, we study jointsourcechannelcoding for bitplane based video coding over wireless channels. We consider using frame-level intra-mode to stop error propagation and using unequal error protection (UEP) t...
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In this paper, we study jointsourcechannelcoding for bitplane based video coding over wireless channels. We consider using frame-level intra-mode to stop error propagation and using unequal error protection (UEP) to combat channel errors. Our focus is on how to optimally select coding modes and find UEP solutions for bitplane based video coding. In particular, we propose an overall end-to-end rate-distortion (R-D) function, which considers not only the source distortion and the channel distortion introduced in the current frame but also the propagated channel distortion from the previous frames. Based on this end-to-end R-D function, we are able to find the optimal solutions for both mode selection and UEP so that an optimal tradeoff between efficiency and robustness can be achieved. Experimental results demonstrate the significant performance gain. (c) 2005 Elsevier Inc. All rights reserved.
In the context of distributed joint source-channel coding, we conceive a joint decoding and estimation scheme for binary Markov sources exhibiting spatio-temporal correlation. The proposed scheme is designed based on ...
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In the context of distributed joint source-channel coding, we conceive a joint decoding and estimation scheme for binary Markov sources exhibiting spatio-temporal correlation. The proposed scheme is designed based on the serial concatenation of a trellis coded modulation (TCM) scheme and a unity-rate code. The symbol-based maximum a posteriori algorithm employed for TCM. decoding is modified in order to exploit the source correlation. The estimation of both the spatial and temporal correlation parameters is performed jointly with the iterative decoding, hence allowing the estimated parameters to he updated after each iteration. Our simulation results reveal that when both the spatial and temporal correlation parameters are unknown, the proposed joint decoding and estimation scheme approaches the performance to the ideal system relying on perfectly known correlation parameters, therefore, demonstrating the superiority of the proposed scheme.
The optimal energy allocations for minimizing the joint symbol error rate for binary signaling of two correlated sources over the orthogonal multiple-access Gaussian channel under joint maximum a priori (MAP) detectio...
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The optimal energy allocations for minimizing the joint symbol error rate for binary signaling of two correlated sources over the orthogonal multiple-access Gaussian channel under joint maximum a priori (MAP) detection are determined. An exact expression for the system's probability of joint symbol error, as well as its union bound, is derived. Analytic minimization of the union bound reveals that the optimal energy allocation coincides with that of nonuniform binary signaling over the single-user additive white Gaussian noise channel. It is also shown numerically that the optimal energies that minimize the union bound also minimize the exact probability of error. Finally, it is shown via simulations for strongly biased sources that the use of joint MAP detection over two independent single-user systems leads to significant gains.
In this letter, we prove a published conjecture on the asymptotic uniformity of the outputs of a convolutional encoder under biased inputs. These results are interesting in light of recent research on jointsource-cha...
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In this letter, we prove a published conjecture on the asymptotic uniformity of the outputs of a convolutional encoder under biased inputs. These results are interesting in light of recent research on joint source-channel coding as well as sourcecoding using turbo codes in which the constituent encoders are convolutional codes. In particular, it is well-known that in many situations a good code should result in a uniform distribution on blocks of consecutive encoded symbols. The results presented here provide insights into the choice of encoders in such scenarios.
jointsource-channel (JSC) decoding based on residual source redundancy is a technique for providing channel robustness to quantized data. Previous work assumed a model equivalent to viewing the encoder/noisy channel ...
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jointsource-channel (JSC) decoding based on residual source redundancy is a technique for providing channel robustness to quantized data. Previous work assumed a model equivalent to viewing the encoder/noisy channel tandem as a discrete hidden Markov model (HMM) with transmitted indices the hidden, states. Here, me generalize this HMM-based (I-D) approach for images, using the more powerful hidden Markov mesh random field (HMMRF) model. While previous state estimation methods for HMMRF's base estimates on only a causal subset of the observed data, our new method uses both causal and anticausal subsets. For JSC-based image decoding, the new method provides significant benefits over several competing techniques.
It is well known that uncoded transmission of a memoryless Gaussian source over a memoryless additive white Gaussian noise channel results in optimal performance theoretically attainable. When there is additional inte...
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It is well known that uncoded transmission of a memoryless Gaussian source over a memoryless additive white Gaussian noise channel results in optimal performance theoretically attainable. When there is additional interference in the channel, uncoded transmission is robust. It achieves the same sensitivity performance as optimal performance, measured using sensitivity results of Pinsker, Prelov, and Verdu.
In this letter, we propose a joint source-channel coding scheme based on polarizing matrix extension (PME-JSCC). The PME-JSCC can combine channel received signal and source side information to form a longer polar code...
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In this letter, we propose a joint source-channel coding scheme based on polarizing matrix extension (PME-JSCC). The PME-JSCC can combine channel received signal and source side information to form a longer polar code. We extend the source encoding matrix and place channel bits on the extended bits. Due to the lower triangular structure of the polarizing matrix, source bits will not be changed by channel bits. The PME-JSCC can obtain enhanced jointsource-channel polarization (JSCP) effect. This effect enhances the reliabilities of both channel bits and source encoded bits simultaneously. The bound on the block error probability for PME-JSCC is improved. And the PME-JSCC can be proved to reach the fundamental limit on JSCC. Simulation results show that the PME-JSCC scheme outperforms the DP-LDPC and the D-Polar codes under the joint successive cancellation list (J-SCL) decoder and can approximate the JSCC finite length bound in the short blocklength regime.
Reliable transmission of a pair of arbitrarily correlated sources over a discrete memoryless cognitive radio channel is studied. We derive a sufficient condition for lossless transmission of such communication scenari...
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Reliable transmission of a pair of arbitrarily correlated sources over a discrete memoryless cognitive radio channel is studied. We derive a sufficient condition for lossless transmission of such communication scenario using superposition coding, correlation preserving technique, random source partition, a binning scheme and joint typicality decoding. This sufficient condition reduces to the known rate regions for interference channels with independent, specially correlated and arbitrarily correlated sources.
The bivariate Gaussian multiterminal sourcecoding problem with transmission over the Gaussian multiple-access channel is studied. We propose the use of low-delay jointsource-channel mappings and show how performance...
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The bivariate Gaussian multiterminal sourcecoding problem with transmission over the Gaussian multiple-access channel is studied. We propose the use of low-delay jointsource-channel mappings and show how performance saturation, which is unavoidable with linear transmission, can be overcome by optimizing the mappings. The optimized mappings are in general nonlinear and perform a combination of hard and soft decision signaling for the error-resilient transmission of analog data.
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