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)


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
Publication date9 Sep 2013
ISBN (Print)978-1-4799-1321-3
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


Workshop2013 IEEE Information Theory Workshop
LocationSchool of Engineering of the University of Seville

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