Automated assessment of video image quality: implications for processing and exploitation

Abstract

Several methods have been developed for quantifying the information potential of imagery exploited by a human observer. The National Imagery Interpretability Ratings Scale (NIIRS) has proven to be a useful standard for intelligence, surveillance, and reconnaissance (ISR) applications. Extensions of this approach to motion imagery have yielded a body of research on the factors affecting interpretability of motion imagery and the development of a Video NIIRS. Automated methods for assessing image interpretability can provide valuable feedback for collection management and guide the exploitation and analysis of the imagery. Prediction models that rely on image parameters, such as the General Image Quality Equation (IQE), are useful for conducting sensor trade studies and collection planning. Models for predicting image quality after image acquisition can provide useful feedback for collection management. Several methods exist for still imagery. This paper explores the development of a similar capability for motion imagery. In particular, we propose methods for predicting the interpretability of motion imagery for exploitation by an analyst. A similar model is considered for automated exploitation.