Keywords
Retina, Sensitivity, Ganglion Cells, Eccentricity
Abstract
Video quality and compression models use the
spatial contrast sensitivity function (CSF), which is solved
based on a linear system approximation. This function measures
the eye’s sensitivity to sinusoid gratings, ignoring the subtle
connectivity and inhomogeniety of cell density across the
visual field. Non-linear aspects of the eye, such as the change
in frequency sensitivity with changing illumination, are not
captured by this simple approximation. We propose Virtual
Eye, a bottom-up approach that models the spatio-temporal
dynamics of the eye across the visual field. Each functional
retinal cell layer in the eye is modeled using non-uniform spatial
cell responses, which can be easily extended to incorporate
complex retinal nonlinearities. Given any grayscale signal input,
Virtual Eye produces a dense output that describes the total
retinal energy transmitted to the brain for each point in the
visual field.
Start Date
15-5-2019 3:30 PM
End Date
15-5-2019 4:00 PM
Virtual Eye: a Spatial-Temporal Bottom-Up Eye Sensitivity Model
Video quality and compression models use the
spatial contrast sensitivity function (CSF), which is solved
based on a linear system approximation. This function measures
the eye’s sensitivity to sinusoid gratings, ignoring the subtle
connectivity and inhomogeniety of cell density across the
visual field. Non-linear aspects of the eye, such as the change
in frequency sensitivity with changing illumination, are not
captured by this simple approximation. We propose Virtual
Eye, a bottom-up approach that models the spatio-temporal
dynamics of the eye across the visual field. Each functional
retinal cell layer in the eye is modeled using non-uniform spatial
cell responses, which can be easily extended to incorporate
complex retinal nonlinearities. Given any grayscale signal input,
Virtual Eye produces a dense output that describes the total
retinal energy transmitted to the brain for each point in the
visual field.