
TL;DR
This paper investigates the binary erasure multiple descriptions problem, proposing coding schemes and bounds that optimize distortion under average and worst-case criteria, with applications to Gaussian sources and the CEO problem.
Contribution
It introduces achievability schemes and optimality results for binary erasure multiple descriptions, and develops a layered coding framework with proven optimality for certain Gaussian cases.
Findings
Achievability schemes based on random binning and MDS codes are optimal for the considered distortion criteria.
A layered coding framework is proposed and shown to be optimal for symmetric scalar Gaussian multiple descriptions.
A new outer bound for multi-terminal source coding and a tight lower bound for the robust binary erasure CEO problem are established.
Abstract
We consider a binary erasure version of the n-channel multiple descriptions problem with symmetric descriptions, i.e., the rates of the n descriptions are the same and the distortion constraint depends only on the number of messages received. We consider the case where there is no excess rate for every k out of n descriptions. Our goal is to characterize the achievable distortions D_1, D_2,...,D_n. We measure the fidelity of reconstruction using two distortion criteria: an average-case distortion criterion, under which distortion is measured by taking the average of the per-letter distortion over all source sequences, and a worst-case distortion criterion, under which distortion is measured by taking the maximum of the per-letter distortion over all source sequences. We present achievability schemes, based on random binning for average-case distortion and systematic MDS (maximum…
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