Abstract
For the Automatic Target Recognition (ATR) algorithm, the quality of the input image sequence can be a major determining factor as to the ATR algorithm's ability to recognize an object. Based on quality, an image can be easy to recognize, barely recognizable, or even mangled beyond recognition. If a determination of the image quality can be made prior to entering the ATR algorithm, then a confidence factor can be applied to the probability of recognition. This confidence factor can be used to rate sensors, to improve quality through selectively preprocessing image sequences prior to applying ATR, or to limit the problem space by determining which image sequences need not be processed by the ATR algorithm. This paper reviews analog and digital forms of image degradation. It looks at traditional quality metric approaches, such as peak signal-to-noise ratio (PSNR). It examines a newer metric based on human vision data. These objective quality metrics can be used as confidence factors primarily in ATR systems that use image sequences degraded due to transmission systems. This paper suggests a more general approach to determining quality using analysis of spatial and temporal vectors where the original input sequence is not explicitly given. The results of this work are demonstrated on a few standard image sequences. (Author)