Linear precoding and decoding for distributed data compression

Azadeh Vosoughi, Anna Scaglione

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Considering two correlated vector sources x, y ∈ ℝN, we address the problem of lossy coding of x with uncoded side information y available at the decoder. The general non-linear mapping between y and x capturing their correlation can be approximated through a linear model y = Hx + n in which n is independent of x. Viewing this model as a virtual communication channel with input x and output y we utilize linear precoding and decoding technique to convert the original vector source coding problem into a set of manageable scalar source coding problems. The scalar source coding problems can be solved using the existing distributed source coding algorithms that are primarily designed for the simple correlation model y = x + n where x and y are scalar jointly Gaussian sources.

Original languageEnglish (US)
Title of host publication2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
PagesIV237-IV240
StatePublished - Dec 1 2006
Externally publishedYes
Event2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France
Duration: May 14 2006May 19 2006

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume4
ISSN (Print)1520-6149

Other

Other2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
CountryFrance
CityToulouse
Period5/14/065/19/06

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ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Vosoughi, A., & Scaglione, A. (2006). Linear precoding and decoding for distributed data compression. In 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings (pp. IV237-IV240). [1660949] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 4).