Video compressive sensing using Gaussian mixture models.

Published

Journal Article

A Gaussian mixture model (GMM)-based algorithm is proposed for video reconstruction from temporally compressed video measurements. The GMM is used to model spatio-temporal video patches, and the reconstruction can be efficiently computed based on analytic expressions. The GMM-based inversion method benefits from online adaptive learning and parallel computation. We demonstrate the efficacy of the proposed inversion method with videos reconstructed from simulated compressive video measurements, and from a real compressive video camera. We also use the GMM as a tool to investigate adaptive video compressive sensing, i.e., adaptive rate of temporal compression.

Full Text

Duke Authors

Cited Authors

  • Yang, J; Yuan, X; Liao, X; Llull, P; Brady, DJ; Sapiro, G; Carin, L

Published Date

  • November 2014

Published In

Volume / Issue

  • 23 / 11

Start / End Page

  • 4863 - 4878

PubMed ID

  • 25095253

Pubmed Central ID

  • 25095253

Electronic International Standard Serial Number (EISSN)

  • 1941-0042

International Standard Serial Number (ISSN)

  • 1057-7149

Digital Object Identifier (DOI)

  • 10.1109/tip.2014.2344294

Language

  • eng