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Image discriminability models predict the visibility of the difference between a pair of images. We compare the ability of two basic models to predict the detectability of objects in natural backgrounds: a multiple channel Cortex transform model with within-channel masking and a single channel contrast sensitivity filter model. Minkowski summation of differences was implemented with three different exponents: 2 (root mean square difference), 4, and infinity (maximum difference). Each method was also tried with a simple contrast gain control normalization. Without contrast normalization, the multiple channel model with a summation exponent of 4 performed best. The predictions of both models improved with contrast normalization. With contrast normalization, at their best exponent of 4, the two models performed equally well.