Models

Models give a response to a single stimulus and are compatible with Metamer and Eigendistortion, and can be turned into Metrics by using the model_metric_factory function.

See Model requirements for more details.

PortillaSimoncelli

Portila-Simoncelli texture statistics.

LGN-inspired Models

These “front end” models are inspired by the lateral geniculate nucleus (LGN; the first non-retinal stage of the primate visual system), come from Berardino et al., 2017, and are nested, increasing in complexity as you move down the list.

LinearNonlinear	Linear-Nonlinear model.
LuminanceGainControl	Linear center-surround followed by luminance gain control and activation.
LuminanceContrastGainControl	Center-surround followed by luminance and contrast gain control, then activation.
OnOff	On-off and off-on center-surround with contrast and luminance gain control.

The following models are used to construct the models above. They are probably most useful in the construction of other, more complex models, but they are compatible with our synthesis methods.

Identity	Simple class that just returns a copy of the image.
Linear	Simplistic linear convolutional model.
Gaussian	Isotropic Gaussian convolutional filter.
CenterSurround	Center-Surround, Difference of Gaussians (DoG) filter model.