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Proceedings Paper

Statistical evaluation of motion-based MTF for full-motion video using the Python-based PyBSM image quality analysis toolbox
Author(s): S. Craig Olson; David Gaudiosi; Andrew Beard; Rich Gueler
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Paper Abstract

As full-motion video (FMV) systems achieve smaller instantaneous fields-of-view (IFOVs), the residual line-of-sight (LOS) motion becomes significantly more influential to the overall system resolving and task performance capability. We augment the AFRL-derived Python-based open-source modeling code pyBSM to calculate distributions of motionbased modulation transfer function (MTF) based on true knowledge of line-of-sight motion. We provide a pyBSMcompatible class that can manipulate either existing or synthesized LOS motion data for frame-by-frame MTF and system performance analysis. The code is used to demonstrate the implementation using both simulated and measured LOS data and highlight discrepancies between the traditional MTF models and LOS-based MTF analysis.

Paper Details

Date Published: 11 May 2018
PDF: 10 pages
Proc. SPIE 10650, Long-Range Imaging III, 106500L (11 May 2018); doi: 10.1117/12.2305406
Show Author Affiliations
S. Craig Olson, L-3 Sonoma EO (United States)
David Gaudiosi, L-3 Sonoma EO (United States)
Andrew Beard, L-3 Sonoma EO (United States)
Rich Gueler, L-3 Sonoma EO (United States)

Published in SPIE Proceedings Vol. 10650:
Long-Range Imaging III
Eric J. Kelmelis, Editor(s)

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