Evaluation of Image Quality Features Via Monotonic Analysis
ARMY RESEARCH LAB ADELPHI MD
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This work introduces novel monotonic analysis to determine whether or not proposed image quality IQ measures are consistent with human measured perceptual quality scores. Specifically, the analysis performs a generalized likelihood ratio test over the H1 hypothesis that the IQ measures and the corresponding perceptual measurements are related via a monotonic function versus the null hypothesis that the functional relationship is arbitrary. This paper evaluates six proposed IQ measures against mean opinion scores using the new monotonic analysis. The next generation of night vision goggles and night scopes will fuse image intensified I2 and long wave infrared LWIR to create a hybrid image that will enable soldiers to better interpret their surroundings during nighttime missions. The key to such systems is the determination of the best image fusion algorithm for a specific task. A number of image fusion algorithms have been proposed in the literature. Currently, a scientific evaluation of such algorithms requires extensive and expensive human perception studies to determine how well soldiers can perform a specific task. What is needed is an image quality IQ measure than can automatically quantify the utility of image fusion algorithms.
- Statistics and Probability
- Computer Programming and Software
- Infrared Detection and Detectors
- Anatomy and Physiology