  Retinal degenerative diseases cause profound visual impairment in more than
10 million people worldwide, and retinal prostheses are being developed to
restore vision to these individuals. Analogous to cochlear implants, these
devices electrically stimulate surviving retinal cells to evoke visual percepts
(phosphenes). However, the quality of current prosthetic vision is still
rudimentary. Rather than aiming to restore "natural" vision, there is potential
merit in borrowing state-of-the-art computer vision algorithms as image
processing techniques to maximize the usefulness of prosthetic vision. Here we
combine deep learning--based scene simplification strategies with a
psychophysically validated computational model of the retina to generate
realistic predictions of simulated prosthetic vision, and measure their ability
to support scene understanding of sighted subjects (virtual patients) in a
variety of outdoor scenarios. We show that object segmentation may better
support scene understanding than models based on visual saliency and monocular
depth estimation. In addition, we highlight the importance of basing
theoretical predictions on biologically realistic models of phosphene shape.
Overall, this work has the potential to drastically improve the utility of
prosthetic vision for people blinded from retinal degenerative diseases.
