  Computer graphics seeks to deliver compelling images, generated within a
computing budget, targeted at a specific display device, and ultimately viewed
by an individual user. The foveated nature of human vision offers an
opportunity to efficiently allocate computation and compression to appropriate
areas of the viewer's visual field, especially with the rise of high resolution
and wide field-of-view display devices. However, while the ongoing study of
foveal vision is advanced, much less is known about how humans process imagery
in the periphery of their vision -- which comprises, at any given moment, the
vast majority of the pixels in the image. We advance computational models for
peripheral vision aimed toward their eventual use in computer graphics. In
particular, we present a dataflow computational model of peripheral encoding
that is more efficient than prior pooling - based methods and more compact than
contrast sensitivity-based methods. Further, we account for the explicit
encoding of "end stopped" features in the image, which was missing from
previous methods. Finally, we evaluate our model in the context of perception
of textures in the periphery. Our improved peripheral encoding may simplify
development and testing of more sophisticated, complete models in more robust
and realistic settings relevant to computer graphics.
