Sampling versus Random Binning for Multiple Descriptions of a Bandlimited Source

Adam Mashiach, Jan Østergaard, Ram Zamir

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

4 Citations (Scopus)
243 Downloads (Pure)

Abstract

Random binning is an efficient, yet complex, coding technique for the symmetric L-description source coding problem. We propose an alternative approach, that uses the quantized samples of a bandlimited source as "descriptions". By the Nyquist condition, the source can be reconstructed if enough samples are received. We examine a coding scheme that combines sampling and noise-shaped quantization for a scenario in which only K < L descriptions or all L descriptions are received. Some of the received K-sets of descriptions correspond to uniform sampling while others to non-uniform sampling. This scheme achieves the optimum rate-distortion performance for uniform-sampling K-sets, but suffers noise amplification for nonuniform-sampling K-sets. We then show that by increasing the sampling rate and adding a random-binning stage, the optimal operation point is achieved for any K-set.
Original languageEnglish
Title of host publication2013 IEEE Information Theory Workshop
Number of pages5
PublisherIEEE
Publication date9 Sep 2013
ISBN (Print)978-1-4799-1321-3
DOIs
Publication statusPublished - 9 Sep 2013
Event2013 IEEE Information Theory Workshop - School of Engineering of the University of Seville, Seville, Spain
Duration: 9 Sep 201313 Sep 2013

Workshop

Workshop2013 IEEE Information Theory Workshop
LocationSchool of Engineering of the University of Seville
CountrySpain
CitySeville
Period09/09/201313/09/2013

Fingerprint Dive into the research topics of 'Sampling versus Random Binning for Multiple Descriptions of a Bandlimited Source'. Together they form a unique fingerprint.

Cite this