  Foveal vision makes up less than 1% of the visual field. The other 99% is
peripheral vision. Precisely what human beings see in the periphery is both
obvious and mysterious in that we see it with our own eyes but can't visualize
what we see, except in controlled lab experiments. Degradation of information
in the periphery is far more complex than what might be mimicked with a radial
blur. Rather, behaviorally-validated models hypothesize that peripheral vision
measures a large number of local texture statistics in pooling regions that
overlap and grow with eccentricity. In this work, we develop a new method for
peripheral vision simulation by training a generative neural network on a
behaviorally-validated full-field synthesis model. By achieving a 21,000 fold
reduction in running time, our approach is the first to combine realism and
speed of peripheral vision simulation to a degree that provides a whole new way
to approach visual design: through peripheral visualization.
