Video compressive sensing using Gaussian mixture models.

TitleVideo compressive sensing using Gaussian mixture models.
Publication TypeJournal Article
Year of Publication2014
AuthorsJ Yang, X Yuan, X Liao, P Llull, DJ Brady, G Sapiro, and L Carin
JournalIeee Transactions on Image Processing : a Publication of the Ieee Signal Processing Society
Volume23
Start Page4863
Issue11
Pagination4863 - 4878
Date Published11/2014
Abstract

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.

DOI10.1109/tip.2014.2344294
Short TitleIeee Transactions on Image Processing : a Publication of the Ieee Signal Processing Society