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An Analysis of Block Sizes for Compressive Sensing Reconstruction Applied in Image Processing Optimisation

Rubens M. B. da S. Lima, Hugo B. S. Araujo, Cleonilson P. de Souza
  • Abstract:
    Signal sampling is a fundamental process of data acquisition systems and several studies have emerged regarding sampling methods following on Nyquist Theorem. Compressive Sensing (CS) proposes sampling of sparse signals with sub-Nyquist sampling rates. In short, CS is composed of a sampling/compress stage and a reconstruction stage. Some algorithms are used where this last. One of these is the Orthogonal Matching Pursuit (OMP) where 2D Discrete Cosine Transform (2D-DCT) can be used. This work compared the application of 2D-DCT transform and CS theory on images as either a whole or split in blocks. As a result, the influence of block size is revealed using the Mean Square Error (MSE) metric for different block sizes.
  • Keywords:
    Compressive Sampling; Image Compression; DCT; Block Size Influence; Innovation
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  • DOI:
    tc10-2022.012

Event details:

  • IMEKO TC:
    TC10
  • Event name:
    TC10 Conference 2022
  • Title:

    18th IMEKO TC10 Conference "Measurement for Diagnostics, Optimisation and Control to Support Sustainability and Resilience"

  • Place:
    Warsaw, POLAND
  • Time:
    26 September 2022 - 27 September 2022