Real-time video image quality estimation supports enhanced tracker performance

Numerous methods exist for quantifying the information potential of imagery exploited by a human observer. The National Imagery Interpretability Ratings Scale (NIIRS) is a useful standard for intelligence, surveillance, and reconnaissance (ISR) applications. Extensions of this approach to motion imagery provide an understanding of the factors affecting interpretability of video data. More recent investigations have shown, however, that human observers and automated processing methods are sensitive to different aspects of image quality. This paper extends earlier research to present a model for quantifying the quality of motion imagery in the context of automated exploitation. In particular, we present a method for predicting the tracker performance and demonstrate the results on a range of video clips. Automated methods for assessing video quality can provide valuable feedback for collection management and guide the exploitation and analysis of the imagery.

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