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A comparison of image quality models and metrics predicting object detection (1995)
Many models and metrics for image quality predict image discriminability, the visibility of the dfference between a pair of images. We compared three such methods for their ability to predict the detectability of objects in natural backgrounds: a Cortex transform model with within-channel masking, a Contrast Sensitivity filter model, and digital image difference metrics. Each method was implemented with three different summation rules: the root mean square difference, Minkowski summation with a power of 4, and maximum difference. The Cortex model with a summation exponent of 4 performed best.
comparison, detection, image, metrics, models, object, predicting, quality
in J. Morreale (Ed.), SID International Symposium Digest of Technical Papers (pp. 45-48). Santa Ana, CA: Society for Information Display
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