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|
- 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.
- Compressive Sampling; Image Compression; DCT; Block Size Influence; Innovation
- Event details
- IMEKO TC:
- Event name:
- TC10 Conference 2022
18th IMEKO TC10 Conference "Measurement for Diagnostics, Optimisation and Control to Support Sustainability and Resilience"
- Warsaw, POLAND
- 26 September 2022 - 27 September 2022