Video compressive sensing using gaussian mixture models

Jianbo Yang; Xin Yuan; Xuejun Liao; Patrick Llull; David J. Brady; Guillermo Sapiro; Lawrence Carin

doi:10.1109/TIP.2014.2344294

Video compressive sensing using gaussian mixture models

Jianbo Yang, Xin Yuan, Xuejun Liao, Patrick Llull, David J. Brady, Guillermo Sapiro, Lawrence Carin

Research output: Contribution to journal › Article › peer-review

170 Scopus citations

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.

Original language	English (US)
Pages (from-to)	4863-4878
Number of pages	16
Journal	IEEE Transactions on Image Processing
Volume	23
Issue number	11
DOIs	https://doi.org/10.1109/TIP.2014.2344294
State	Published - Nov 1 2014
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-02-09

Access to Document

10.1109/TIP.2014.2344294

Cite this

@article{2da0d9ba1e354dd4920766c5265685f8,

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.",

author = "Jianbo Yang and Xin Yuan and Xuejun Liao and Patrick Llull and Brady, {David J.} and Guillermo Sapiro and Lawrence Carin",

note = "Generated from Scopus record by KAUST IRTS on 2021-02-09",

year = "2014",

month = nov,

day = "1",

doi = "10.1109/TIP.2014.2344294",

language = "English (US)",

volume = "23",

pages = "4863--4878",

journal = "IEEE Transactions on Image Processing",

issn = "1057-7149",

publisher = "Institute of Electrical and Electronics Engineers",

number = "11",

}

TY - JOUR

T1 - Video compressive sensing using gaussian mixture models

AU - Yang, Jianbo

AU - Yuan, Xin

AU - Liao, Xuejun

AU - Llull, Patrick

AU - Brady, David J.

AU - Sapiro, Guillermo

AU - Carin, Lawrence

N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09

PY - 2014/11/1

Y1 - 2014/11/1

N2 - 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.

AB - 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.

UR - http://ieeexplore.ieee.org/document/6868277/

UR - http://www.scopus.com/inward/record.url?scp=84908006811&partnerID=8YFLogxK

U2 - 10.1109/TIP.2014.2344294

DO - 10.1109/TIP.2014.2344294

M3 - Article

SN - 1057-7149

VL - 23

SP - 4863

EP - 4878

JO - IEEE Transactions on Image Processing

JF - IEEE Transactions on Image Processing

IS - 11

ER -

Video compressive sensing using gaussian mixture models

Abstract

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this