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Proceedings Paper

Automated FMV image quality assessment based on power spectrum statistics
Author(s): Andrew Kalukin
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Paper Abstract

Factors that degrade image quality in video and other sensor collections, such as noise, blurring, and poor resolution, also affect the spatial power spectrum of imagery. Prior research in human vision and image science from the last few decades has shown that the image power spectrum can be useful for assessing the quality of static images. The research in this article explores the possibility of using the image power spectrum to automatically evaluate full-motion video (FMV) imagery frame by frame. This procedure makes it possible to identify anomalous images and scene changes, and to keep track of gradual changes in quality as collection progresses. This article will describe a method to apply power spectral image quality metrics for images subjected to simulated blurring, blocking, and noise. As a preliminary test on videos from multiple sources, image quality measurements for image frames from 185 videos are compared to analyst ratings based on ground sampling distance. The goal of the research is to develop an automated system for tracking image quality during real-time collection, and to assign ratings to video clips for long-term storage, calibrated to standards such as the National Imagery Interpretability Rating System (NIIRS).

Paper Details

Date Published: 19 May 2015
PDF: 20 pages
Proc. SPIE 9460, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications XII, 94600N (19 May 2015); doi: 10.1117/12.2182615
Show Author Affiliations
Andrew Kalukin, National Geospatial-Intelligence Agency (United States)

Published in SPIE Proceedings Vol. 9460:
Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications XII
Daniel J. Henry; Gregory J. Gosian; Davis A. Lange; Dale Linne von Berg; Thomas J. Walls; Darrell L. Young, Editor(s)

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