title: Video compressive sensing using Gaussian mixture models.
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.
authors.forename: Jianbo
authors.initials: J
authors.lastname: Yang
authors.forename: Xin
authors.initials: X
authors.lastname: Yuan
authors.forename: Xuejun
authors.initials: X
authors.lastname: Liao
authors.forename: Patrick
authors.initials: P
authors.lastname: Llull
authors.forename: David J
authors.initials: DJ
authors.lastname: Brady
authors.forename: Guillermo
authors.initials: G
authors.lastname: Sapiro
authors.forename: Lawrence
authors.initials: L
authors.lastname: Carin