Analysis of the Measurement Matrix in Directional Predictive Coding for Compressive Sensing of Medical Images
Christinal, Hepzibah A. (Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India)
Kowsalya, G. (Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India)
Chandy D., Abraham (Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India)
Jebasingh, Stephenraj (Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India)
Bajaj, Chandrajit (University of Texas)

Fecha:	2021
Resumen:	Compressive sensing of 2D signals involves three fundamental steps: sparse representation, linear measurement matrix, and recovery of the signal. This paper focuses on analyzing the efficiency of various measurement matrices for compressive sensing of medical images based on theoretical predictive coding. During encoding, the prediction is efficiently chosen by four directional predictive modes for block-based compressive sensing measurements. In this work, Gaussian, Bernoulli, Laplace, Logistic, and Cauchy random matrices are used as the measurement matrices. While decoding, the same optimal prediction is de-quantized. Peak-signal-to-noise ratio and sparsity are used for evaluating the performance of measurement matrices. The experimental result shows that the spatially directional predictive coding (SDPC) with Laplace measurement matrices performs better compared to scalar quantization (SQ) and differential pulse code modulation (DPCM) methods. The results indicate that the Laplace measurement matrix is the most suitable in compressive sensing of medical images.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Coding and compression ; Medical image analysis ; Statistical pattern recognition ; Applications
Publicado en:	ELCVIA. Electronic letters on computer vision and image analysis, Vol. 20 Núm. 2 (2021) , p. 102-113 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/1412
DOI: 10.5565/rev/elcvia.1412

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