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Multimedia Signals and Systems pp 233–254Cite as

Compressive Sensing

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Abstract

In order to bring a new perspective into the multimedia data acquisition, compression, and representation, the compressive sensing theory and its application to multimedia data have been considered. The compressive sensing represents an alternative to the standard signal acquisition and processing methods, which is based on the fact that the signal under certain assumptions can be reconstructed from far lower number of random samples than it is required by the Shannon-Nyquist sampling theorem. It has been shown that the compressive sensing can be successfully applied to reconstruct images and audio signals from a small number of random measurements. In order to provide a better insight into the compressive sensing theory, the concepts are explained on the simple examples. The mathematical algorithms for solving the optimization problems, such as interior point methods, total variation minimization, etc., are discussed and elaborated. An application of compressed sensing in the time-frequency domain is presented as well.

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Authors and Affiliations

  1. University of Montenegro, Džordža Vašingtona bb, Podgorica, Montenegro

    Srdjan Stanković & Irena Orović

  2. University of Pittsburgh, Benedum Hall, Pittsburgh, PA, USA

    Ervin Sejdić

Authors
  1. Srdjan Stanković
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  2. Irena Orović
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  3. Ervin Sejdić
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Correspondence to Srdjan Stanković .

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© 2012 Springer Science+Business Media, LLC

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Stanković, S., Orović, I., Sejdić, E. (2012). Compressive Sensing. In: Multimedia Signals and Systems. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-4208-0_6

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  • DOI: https://doi.org/10.1007/978-1-4614-4208-0_6

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4614-4207-3

  • Online ISBN: 978-1-4614-4208-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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