Networked Multiple Description Estimation and Compression with Resource Scalability

Details Networked Multiple Description Estimation and Compression with Resource Scalability Networked Multiple Description Estimation and Compression with Resource Scalability

2007-08-27

arxiv.org/abs/0708.3531v1

Computer Science

Information Theory

Scientific paper

We present a joint source-channel multiple description (JSC-MD) framework for resource-constrained network communications (e.g., sensor networks), in which one or many deprived encoders communicate a Markov source against bit errors and erasure errors to many heterogeneous decoders, some powerful and some deprived. To keep the encoder complexity at minimum, the source is coded into K descriptions by a simple multiple description quantizer (MDQ) with neither entropy nor channel coding. The code diversity of MDQ and the path diversity of the network are exploited by decoders to correct transmission errors and improve coding efficiency. A key design objective is resource scalability: powerful nodes in the network can perform JSC-MD distributed estimation/decoding under the criteria of maximum a posteriori probability (MAP) or minimum mean-square error (MMSE), while primitive nodes resort to simpler MD decoding, all working with the same MDQ code. The application of JSC-MD to distributed estimation of hidden Markov models in a sensor network is demonstrated. The proposed JSC-MD MAP estimator is an algorithm of the longest path in a weighted directed acyclic graph, while the JSC-MD MMSE decoder is an extension of the well-known forward-backward algorithm to multiple descriptions. Both algorithms simultaneously exploit the source memory, the redundancy of the fixed-rate MDQ, and the inter-description correlations. They outperform the existing hard-decision MDQ decoders by large margins (up to 8dB). For Gaussian Markov sources, the complexity of JSC-MD distributed MAP sequence estimation can be made as low as that of typical single description Viterbi-type algorithms.

Affiliated with

Also associated with

No associations

Rating

Networked Multiple Description Estimation and Compression with Resource Scalability does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Networked Multiple Description Estimation and Compression with Resource Scalability, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Networked Multiple Description Estimation and Compression with Resource Scalability will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-125953