Channel Optimized Distributed Multiple Description Coding

Abstract

In this paper, channel optimized distributed multiple description vector quantization (CDMD) schemes are presented for distributed source coding in symmetric and asymmetric settings. The CDMD encoder is designed using a deterministic annealing approach over noisy channels with packet loss. A minimum mean squared error asymmetric CDMD decoder is proposed for effective reconstruction of a source, utilizing the side information (SI) and its corresponding received descriptions. The proposed iterative symmetric CDMD decoder jointly reconstructs the symbols of multiple correlated sources. Two types of symmetric CDMD decoders, namely the estimated-SI and the soft-SI decoders, are presented which respectively exploit the reconstructed symbols and a posteriori probabilities of other sources as SI in iterations. In a multiple source CDMD setting, for reconstruction of a source, three methods are proposed to select another source as its SI during the decoding. The methods operate based on minimum physical distance (in a wireless sensor network setting), maximum mutual information and minimum end-to-end distortion. The performance of the proposed systems and algorithms are evaluated and compared in detail.EDICS: SEN-DCSC Distributed channel and source coding 2

Distributed Channel-Aware Quantization Based on Maximum Mutual Information

“…For distributed quantization, channel-optimized quantizer design has been developed for hypothesis testing by minimizing the Bayesian cost [30, 31]. Recently, the distributed channel-aware quantizer design for multiple correlated sources has been addressed by [32, 33], where M source encoders are designed to quantize M correlated sources, in presence of noisy communication channels. Reference [34] discusses the problem under a total power constraint and designs the quantizers by minimizing the signal distortion in the receiver.…”

“…Reference [34] discusses the problem under a total power constraint and designs the quantizers by minimizing the signal distortion in the receiver. For multimedia applications in distributed networks, the multiple description coding has been used to fight the channel loss [33]. References [35–37] have addressed the effect of imperfect transmission channels in the multiple description coding algorithms for the application of distributed video transmission.…”

On polar multiple description coding

“…In the literature, many methods have been provided for the MD, for example, the index assignment table‐based approach [2], trellis‐based method approach [3], lattice‐based approach [4] and the transform coding approach [5]. Also, MD has been exploited in many scenarios including the distributed source coding, for example, [6] and references in.…”