Video compressive sensing using multiple measurement vectors

Michael Iliadis, Jeremy Watt, Leonidas Spinoulas, Aggelos K Katsaggelos

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

2 Scopus citations

Abstract

Compressive Sensing (CS) suggests that, under certain conditions, a signal can be reconstructed using a small number of incoherent measurements. We propose a novel video CS framework based on Multiple Measurement Vectors (MMV) which is suitable for signals with temporal correlation such as video sequences. In addition, a CS circulant matrix is employed for fast reconstruction. Furthermore, the proposed framework allows the number of CS measurements associated with each frame to be chosen in the decoder rather than the encoder offering robustness compared to the multi-scale approaches. Experimental results on two video sequences exhibiting fast motion and occlusions, show the advantages of the proposed method over the current state-of-the-art in video CS.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages136-140
Number of pages5
DOIs
StatePublished - Dec 1 2013
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: Sep 15 2013Sep 18 2013

Other

Other2013 20th IEEE International Conference on Image Processing, ICIP 2013
CountryAustralia
CityMelbourne, VIC
Period9/15/139/18/13

Keywords

  • circulant matrix
  • fast motion
  • multiple measurement vectors
  • Video compressive sensing

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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