Statistical evaluation of motion-based MTF for full-motion video using the Python-based PyBSM image quality analysis toolbox

S. Craig Olson; David Gaudiosi; Andrew Beard; Rich Gueler

doi:10.1117/12.2305406

11 May 2018 Statistical evaluation of motion-based MTF for full-motion video using the Python-based PyBSM image quality analysis toolbox

S. Craig Olson, David Gaudiosi, Andrew Beard, Rich Gueler

Author Affiliations +

Proceedings Volume 10650, Long-Range Imaging III; 106500L (2018) https://doi.org/10.1117/12.2305406
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

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.

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S. Craig Olson, David Gaudiosi, Andrew Beard, and Rich Gueler "Statistical evaluation of motion-based MTF for full-motion video using the Python-based PyBSM image quality analysis toolbox", Proc. SPIE 10650, Long-Range Imaging III, 106500L (11 May 2018); https://doi.org/10.1117/12.2305406

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