Figure 1.

Possible coarse encoding strategies for peripheral vision (a demo). (a) An original image patch, to be viewed peripherally. Suppose, hypothetically, that we want to represent this patch with only 1000 numbers. (b) Subsampling to reduce to a 32x32 image. Clearly this would be a poor representation. One can tell that the original stimulus consisted of 7 items in an array, but we have no idea that those items were made up of lines, nor that they formed letters. (c) Representation by local orientation at multiple scales, as in early visual cortex (V1), followed by reduction to 1000 numbers leads to a similarly poor result. This encoding used the discrete cosine transform; using a more biologically plausible wavelet transform leads to similar (but worse) results. (d) For the same 1000 numbers, one can encode a whole bunch of summary statistics, e.g.: the correlation of responses of V1-like cells across location, orientation, and scale; phase correlation, marginal statistics of the luminance, and autocorrelation of the luminance. Here we visualize the information available in this representation by synthesizing a new “sample” with the same statistics as those measured from (a), using a technique (and statistics) from Portilla & Simoncelli (2000). This encoding captures much more useful information about the original stimulus. Figure originally published in Rosenholtz (2011).