# Objective Prediction of Human Visual Acuity Using Image Quality Metrics
## 1 Introduction
They are _MSE_, _PSNR_, _SSIM_, the Multiscale Structural Similarity Index (_MSSSIM_) [9], the peak signal-to-noise ratio based on the HVS (_PSNR-HVS_) [10], gradient magnitude similarity deviation (_GMSD_) [11] and the Feature Similarity Index (_FSIM_) [12]. These metrics were chosen because they are fast, easy to implement, and sufficiently confirmed. We establish a relation between image quality metrics, hitherto restricted to the field of signal and image processing, and the quality of vision concept, quantified by monocular VA. As far as we know, no works use static image or video quality metrics to study the quality of human vision, and only two references that link both aspects appear in the literature. [PERSON] [13] discussed a possible relation between some image processing metrics and subjects' visual quality. The study was performed for two sets of metrics: those based on comparing images and those based on the optical transfer function. The metric that best correlated with the evaluation of subjects' ametropia was entropy. Later in [14], some of the authors adapted the _MSSIM_ metric to the visual process (_VMSSIM_) with two subjects (one myopic and another hyperopic), both before and after being treated by LASIK surgery, to objectively predict their visual quality. Several studies have been recently published on quantifying the quality of an image, but by combining several full reference metrics [15; 16; 17]. Their results reveal that these combinations seem to exceed individual metrics when predicting the quality of an image. We approached the problem of assessing HVS quality from the same point of view and proposed combining metrics to predict VA. Most psychophysical experiments are performed with relatively simple patterns, such as blobs, sinusoidal bars or grids, letters, etc. For example, the contrast sensitivity function is usually obtained from thresholds with global sinusoidal images. However, all these patterns are simpler than real-world images,which can be considered as a superposition of a larger number of simple patterns. VA measures in some ways the degradation of a subject's optimal visual quality. The objective of this work is to relate digital image processing metrics, that use natural images, with VA, since there must be a relationship between them. Subjective VA tests measured using opto-types comprises both physical and neural factors. Natural images are necessary to quantify in some manner for human tolerance to image degradation because using optotypes as images (not natural images) and the wavefront aberration (only physical factors) would not be able to model neural or even subjective factors that in some manners are present in natural images evaluated with _MOS_. The manuscript is structured as follows. Section 2 describes the procedure. First, an image database is defined from existing ones. Next, details of the subjects participating in the study and measurements are provided. Then, the calculation of the PSF and the image of the eye are explained. The method section ends by describing the metrics calculation and defining the fitting to subjective VA and conditions. The results appear in the third section and the conclusions are finally stated. The metrics are described in Appendix A.