Predicting detection performance on security X-ray images as a function of image quality.

Developing methods to predict how image quality affects task performance is a topic of great interest in many applications. While such studies have been performed in the medical imaging community, little work has been reported in the security X-ray imaging literature. In this work, we develop models that predict the effect of image quality on the detection of improvised explosive device (IED) components by bomb technicians in images taken using portable X-ray systems. Using a newly developed NIST-LIVE X-Ray Task Performance Database, we created a set of objective algorithms that predict bomb technician detection performance based on measures of image quality. Our basic measures are traditional Image Quality Indicators (IQIs) and perceptually-relevant Natural Scene Statistics (NSS)-based measures that have been extensively used in visible light (VL) image quality prediction algorithms. We show that these measures are able to quantify the perceptual severity of degradations and can predict the performance of expert bomb technicians to identify threats. Combining NSS- and IQI-based measures yields even better task performance prediction than either of these methods independently. We also developed a new suite of statistical task prediction models that we refer to as Quality Inspectors of X-ray images (QUIX), which we believe to be the first NSS-based model for security X-ray images. We also show that QUIX can be used to reliably predict conventional IQI metric values on distorted X-ray images.