Simulating visibility under reduced acuity and contrast sensitivity

Abstract

Architects and lighting designers have difficulty designing spaces that are accessible to those with low vision, since the complex nature of most architectural spaces requires a site-specific analysis of the visibility of mobility hazards and key landmarks needed for navigation. We describe a method that can be utilized in the architectural design process for simulating the effects of reduced acuity and contrast on visibility. The key contribution is the development of a way to parameterize the simulation using standard clinical measures of acuity and contrast sensitivity. While these measures are known to be imperfect predictors of visual function, they provide a way of characterizing general levels of visual performance that is familiar to both those working in low vision and our target end-users in the architectural and lighting-design communities. We validate the simulation using a letter-recognition task.

Fig. 1

The Chung & Legge [15] CSF is an asymmetric parabola when plotted in f_l − S_l space. The plotted values show two instances of the CSF, one shifted left (lower acuity) and down (lower contrast sensitivity) compared to the other.

Fig. 2

(a) Contrast sensitivity plots for different peak contrast sensitivities, but the same peak contrast sensitivity frequencies. (b) Contrast sensitivity plots for different peak contrast sensitivities, but the same acuity as measured by cutoff frequency.

Fig. 3

CSF cutoff frequency as a function of peak contrast sensitivity for a peak contrast sensitivity frequency corresponding to normal vision.

Fig. 4

Screenshots of two stimuli used to evaluate the setting of FPN. Figure 4a shows logMAR 1.3 sized characters, filtered to simulate an acuity of logMAR 1.2. Figure 4b shows logMAR 1.1 sized characters, filtered to simulate an acuity of logMAR 1.2. Letters in Figure 4a are readily recognizable while those in Figure 4b are not. (FPN = 0.915 cycles/degree for both examples.)

Fig. 5

Empirically determined acuity for simulated low vision with FPN = 0.915 cycles/degree

Fig. 6

Empirically determined smallest legible characters for reduced acuity and contrast sensitivity.

Fig. 7

Empirically determined contrast sensitivity for simulated normal vision.

Fig. 8

(a) Original logMAR chart, with third line from top corresponding to logMAR 1.1 and the fourth line from the top corresponding to logMAR 0.9. For correct character size, view the chart from a distance equivalent to 3.33 times the width of the chart image. (b) Original logMAR chart, filtered to simulate an acuity of logMAR 1.0. The third line is readable, the fourth line is not.

Fig. 9

(a) Vertical bars of same width with two different contrasts with respect to the background, (b) low-vision simulation using [6] thresholding, (c) low-vision simulation using improved thresholding.

Fig. 10

(a) Luminance profile of Figure 9a, (b) plot of one of the bands produced by the low vision simulation filter, (c) [6] style thresholding of band, (d) improved thresholding of band.

Fig. 11

Examples of simulated loss of acuity and contrast sensitivity for RADIANCE models of a Washington DC Metro station (left column) and the model modified to provide improved lighting (right column).