Optimal multiple description transform coding of Gaussian vectors

Vivek K. Goyal, Jelena Kovacevic

Research output: Contribution to journalConference article

Abstract

Multiple description coding (MDC) is source coding for multiple channels such that a decoder which receives an arbitrary subset of the channels may produce a useful reconstruction. Orchard et. a. [1] proposed a transform coding method for MDC of pairs of independent Gaussian random variables. This paper provides a general framework which extends multiple description transforms coding (MDTC) to any number of variables and expands the set of transforms which are considered. Analysis of the general case is provided, which can be used to numerically design optimal MDTC systems. The case of two variables sent over two channels is analytically optimized in the most general setting where channel failures need not have equal probability or be independent. It is shown that when channel failures are equally probable and independent, the transforms used in [1] are in the optimal set, but many other choices are possible. A cascade structure is presented which facilitates low-complexity design, coding, and decoding for a system with a large number of variables.

Original languageEnglish (US)
Pages (from-to)388-397
Number of pages10
JournalData Compression Conference Proceedings
StatePublished - Jan 1 1998
EventProceedings of the 1998 Data Compression Conference, DCC - Snowbird, UT, USA
Duration: Mar 30 1998Apr 1 1998

Fingerprint

Orchards
Random variables
Decoding
Optimal design

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

Optimal multiple description transform coding of Gaussian vectors. / Goyal, Vivek K.; Kovacevic, Jelena.

In: Data Compression Conference Proceedings, 01.01.1998, p. 388-397.

Research output: Contribution to journalConference article

@article{809121adde5a4cf3b51511c7e7b8e2c7,
title = "Optimal multiple description transform coding of Gaussian vectors",
abstract = "Multiple description coding (MDC) is source coding for multiple channels such that a decoder which receives an arbitrary subset of the channels may produce a useful reconstruction. Orchard et. a. [1] proposed a transform coding method for MDC of pairs of independent Gaussian random variables. This paper provides a general framework which extends multiple description transforms coding (MDTC) to any number of variables and expands the set of transforms which are considered. Analysis of the general case is provided, which can be used to numerically design optimal MDTC systems. The case of two variables sent over two channels is analytically optimized in the most general setting where channel failures need not have equal probability or be independent. It is shown that when channel failures are equally probable and independent, the transforms used in [1] are in the optimal set, but many other choices are possible. A cascade structure is presented which facilitates low-complexity design, coding, and decoding for a system with a large number of variables.",
author = "Goyal, {Vivek K.} and Jelena Kovacevic",
year = "1998",
month = "1",
day = "1",
language = "English (US)",
pages = "388--397",
journal = "Proceedings of the Data Compression Conference",
issn = "1068-0314",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Optimal multiple description transform coding of Gaussian vectors

AU - Goyal, Vivek K.

AU - Kovacevic, Jelena

PY - 1998/1/1

Y1 - 1998/1/1

N2 - Multiple description coding (MDC) is source coding for multiple channels such that a decoder which receives an arbitrary subset of the channels may produce a useful reconstruction. Orchard et. a. [1] proposed a transform coding method for MDC of pairs of independent Gaussian random variables. This paper provides a general framework which extends multiple description transforms coding (MDTC) to any number of variables and expands the set of transforms which are considered. Analysis of the general case is provided, which can be used to numerically design optimal MDTC systems. The case of two variables sent over two channels is analytically optimized in the most general setting where channel failures need not have equal probability or be independent. It is shown that when channel failures are equally probable and independent, the transforms used in [1] are in the optimal set, but many other choices are possible. A cascade structure is presented which facilitates low-complexity design, coding, and decoding for a system with a large number of variables.

AB - Multiple description coding (MDC) is source coding for multiple channels such that a decoder which receives an arbitrary subset of the channels may produce a useful reconstruction. Orchard et. a. [1] proposed a transform coding method for MDC of pairs of independent Gaussian random variables. This paper provides a general framework which extends multiple description transforms coding (MDTC) to any number of variables and expands the set of transforms which are considered. Analysis of the general case is provided, which can be used to numerically design optimal MDTC systems. The case of two variables sent over two channels is analytically optimized in the most general setting where channel failures need not have equal probability or be independent. It is shown that when channel failures are equally probable and independent, the transforms used in [1] are in the optimal set, but many other choices are possible. A cascade structure is presented which facilitates low-complexity design, coding, and decoding for a system with a large number of variables.

UR - http://www.scopus.com/inward/record.url?scp=0031702892&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031702892&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0031702892

SP - 388

EP - 397

JO - Proceedings of the Data Compression Conference

JF - Proceedings of the Data Compression Conference

SN - 1068-0314

ER -