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Previously, in (Hermundstad et al., 2014), we showed that when sampling islimiting, the efficient coding principle leads to a "variance is salience"hypothesis, and that this hypothesis accounts for visual sensitivity to binaryimage statistics. Here, using extensive new psychophysical data and imageanalysis, we show that this hypothesis accounts for visual sensitivity to alarge set of grayscale image statistics at a striking level of detail, and alsoidentify the limits of the prediction. We define a 66-dimensional space oflocal grayscale light-intensity correlations, and measure the relevance of eachdirection to natural scenes. The "variance is salience" hypothesis predictsthat two-point correlations are most salient, and predicts their relativesalience. We tested these predictions in a texture-segregation task usingun-natural, synthetic textures. As predicted, correlations beyond second orderare not salient, and predicted thresholds for over 300 second-ordercorrelations match psychophysical thresholds closely (median fractional error<0.13).

Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures