Use of a local cone model to predict essential CSF light adaptation behavior used in the design of luminance quantization nonlinearities

The human visual system’s luminance nonlinearity ranges continuously from square root behavior in the very dark, gamma-like behavior in dim ambient, cube-root in office lighting, and logarithmic for daylight ranges. Early display quantization nonlinearities have been developed based on luminance bipartite JND data. More advanced approaches considered spatial frequency behavior, and used the Barten light-adaptive Contrast Sensitivity Function (CSF) modelled across a range of light adaptation to determine the luminance nonlinearity (e.g., DICOM, referred to as a GSDF {grayscale display function}). A recent approach for a GSDF, also referred to as an electrical-to-optical transfer function (EOTF), using that light-adaptive CSF model improves on this by tracking the CSF for the most sensitive spatial frequency, which changes with adaptation level. We explored the cone photoreceptor’s contribution to the behavior of this maximum sensitivity of the CSF as a function of light adaptation, despite the CSF’s frequency variations and that the cone’s nonlinearity is a point-process. We found that parameters of a local cone model could fit the max sensitivity of the CSF model, across all frequencies, and are within the ranges of parameters commonly accepted for psychophysicallytuned cone models. Thus, a linking of the spatial frequency and luminance dimensions has been made for a key neural component. This provides a better theoretical foundation for the recently designed visual signal format using the aforementioned EOTF.