Distance v.s. Resolution: Neuromapping of Effective Resolution onto Physical Distance

Abstract

The main focus of this work is on determining the effective resolution of a face image on the retina when the face is at a particular distance from the eye. Despite its straightforward articulation, arriving at a satisfactory solution might be unexpectedly challenging. The relationship between viewing distance and effective resolution cannot be easily obtained through contrast sensitivity, Snellen acuity, or even photoreceptor packing density in the fovea. We used theoretical considerations to establish preliminary guidelines and then showed participants images of different resolutions at various viewing distances. At each distance, participants were expected to perform an “odd one out” task. During the experiment, participants were asked to identify the image that differed from the others in a 2×2 grid, with image resolution as the sole variable. As the study progressed, viewing distance was gradually reduced, and participants became more adept at distinguishing finer resolution disparities between the three standard images and the outlier. The data collected enabled us to determine the upper and lower limits of the available effective resolution for human vision under normal conditions, as a function of viewing distance. One of the interesting observations is that human performance in blur detection is notably superior to what a theoretical model based on projected image size, cone density, and foveal extent predicts, especially at close ranges. Therefore, we propose that future theoretical models must account for non-uniform in–fovea density and the less pronounced decline in acuity outside the fovea to establish a reliable relationship between viewing distance and perceived image characteristics. The<distance:effective-resolution>mapping has practical applications, as it allows for a direct comparison of human face recognition performance across different levels of blur and viewing distance. It also enables us to systematically compare human performance to that of machine vision systems using resolution as a common factor.