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March 12, Tuesday
12:00 – 13:00

The bulk of previous research on global image statistics has focused either on first order statistics such as luminance and contrast distributions, or on second order statistics such as the autocorrelation, or equivalently the power spectrum. While numerous links between global statistics of natural images and the functional architecture of biological visual systems have been established, it is widely agreed that perceptually important details such as edges and object boundaries are not captured by such low order statistics but rather by higher order ones. In this talk, I'll address the global first and second order statistics, but unlike previous studies, we consider them jointly. For this purpose we have developed an algorithm that produces random images with prescribed first and second order statistics. That is, given a natural image, the algorithm generates a new image with a similar distribution and spatial correlations of color as those of the original one, but otherwise being random. Our surprising observation is that the original images might be reconstructed only from their low order statistics. We conclude that the perceptual information content of first and second order statistics of natural images, when considered jointly, is greater than has been previously appreciated, and therefore might be utilized for efficient representations and processing of visual information.A joint work with prof. Ohad Ben-Shahar.