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Analysis of the Measurement Matrix in Directional Predictive Coding for Compressive Sensing of Medical Images
Author(s) -
Hepzibah Christinal A,
G. Kowsalya,
Abraham Chandy D,
S. Jebasingh,
Chandrajit Bajaj
Publication year - 2022
Publication title -
elcvia. electronic letters on computer vision and image analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.15
H-Index - 11
ISSN - 1577-5097
DOI - 10.5565/rev/elcvia.1412
Subject(s) - compressed sensing , computer science , algorithm , laplace transform , signal reconstruction , gaussian , matrix (chemical analysis) , decoding methods , quantization (signal processing) , mathematics , signal processing , physics , mathematical analysis , digital signal processing , materials science , quantum mechanics , computer hardware , composite material
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.