Okkeun Lee

Introduction

Contrast sensitivity function (CSF) is a subjective measurement of an ability of the visual system to detect a low contrast pattern stimuli. The stimuli considered usually are vertical sinusoidal Gabor patches of decreasing shades of black to grey. This use of sine wave gratings was first introduced in vision by Schade (1956) and was subsequently used by early investigators to measure basic visual sensitivity (Westheimer, 1960; DePalma and Lowry, 1962; Campbell and Robson, 1968). The resulting measurement is used to validate the representation of the eyes' visual performance as it complements the visual acuity. CSF is synonyms with an audiogram where a person’s highest detectable pitch is measured and well as the ability to hear all lower pitches.

The CSF measurements are usually acquired with small patches of sinusoidal grating designed to fall within few central degrees of the visual field. It is well known that the CSF decreases as one measures contrast sensitivity at increasingly peripheral locations in the visual field. The reasons for such decreased CSF is attributed to a number of neural factors. The human eye is structured such that the distribution of the cone mosaic falls off rapidly as a function of visual eccentricity, so that there are fewer sensors available to detect and encode the incoming stimuli. Towards the periphery the amount of retinal ganglion cells’ density falls as well. This structure of the cones is also a key factor in deciding the CSF. In particular, for this project, we make an effort to explore the role of inference engines in shaping the CSF.

Background

The human visual system’s CSF or the modulation transfer function (MTF) fundamentally characterises eye’s spatial frequency response and thus, one may think of it as a bandpass filter. This bandpass nature, however, is determined by a variety of factors such as the front-end optics, cone distribution geometry and the neural mechanism that is responsible for this interpretation which is typically inferred using the machine learning techniques such as the KNN and SVM.

The methods of CSF measurement usually employ sine-wave gratings of a fixed frequency. By varying the frequency, a set of stimuli are constructed. The response of the eye to these stimuli are then determined. This procedure is repeated for a large number of grating frequencies. The resulting curve is called the contrast sensitivity function and is illustrated in Fig. 1

From the measurements, contrast sensitivity score is determined such that it is equal to (1/threshold) for the given spatial form. The contrast sensitivity scores obtained for each of the sine-wave gratings examined are then plotted as a function of target spatial frequency yielding the contrast sensitivity function (CSF). Some typical CSF's are depicted in Figure 1 below shows the typical inverted-U shape of the CSF on logarithmic axes.

The authors have demonstrated that the CSF attains its maximum while using foveal vision as indicated in In Fig 2. They observed that the CSF reduces with increasing retinal eccentricity. Further, the CSF declines with eccentricity is greater for higher spatial frequencies but more gradual for lower spatial frequencies.