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Maximum frame rate video acquisition using adaptive compressed sensing

  • Author / Creator
    Liu, Zhaorui
  • Compressed sensing is a novel technology to acquire and reconstruct sparse signals below the Nyquist rate. This thesis explores the temporal redundancy in videos, and proposes a block-based adaptive framework for compressed video sampling. The proposed framework classifies blocks into different types depending on temporal correlation, and adjusts the sampling and reconstruction strategy accordingly. This framework also considers the texture complexity of regions, and adaptively adjusts the number of measurements collected. A frame rate selection module is included to select the maximum achievable frame rate under the hardware sampling rate and the perceptual quality constraints. Simulation results show that compared to the raster scan, the proposed framework can increase the frame rate by up to six times depending on the scene and the video quality constraints. A 1.5~7.8dB gain in the average PSNR of the reconstructed frames is observed when compared with prior works on compressed video sensing.

  • Subjects / Keywords
  • Graduation date
    2011-06
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R36D98
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Electrical and Computer Engineering
  • Supervisor / co-supervisor and their department(s)
    • Zhao, Vicky (Electrical and Computer Engineering)
  • Examining committee members and their departments
    • Han, Bin (Mathematical and Statistical Sciences)
    • Mandal, Mrinal (Electrical and Computer Engineering)
    • Zhao, Vicky (Electrical and Computer Engineering)