Predicting the Blur Visual Discomfort for Natural Scenes by the Loss of Positional Information

TL;DR

This paper introduces a model linking visual discomfort from blur to the loss of positional Fisher Information, predicting discomfort based on natural scene features and validated against subjective ratings.

Contribution

It proposes a novel model that predicts visual discomfort caused by blur through the loss of positional Fisher Information, extending to various blur types.

Findings

Model accurately predicts subjective discomfort ratings

Fisher Information loss correlates with perceived blur discomfort

Model fits data across different experimental protocols

Abstract

The perception of the blur due to accommodation failures, insufficient optical correction or imperfect image reproduction is a common source of visual discomfort, usually attributed to an anomalous and annoying distribution of the image spectrum in the spatial frequency domain. In the present paper, this discomfort is attributed to a loss of the localization accuracy of the observed patterns. It is assumed, as a starting perceptual principle, that the visual system is optimally adapted to pattern localization in a natural environment. Thus, since the best possible accuracy of the image patterns localization is indicated by the positional Fisher Information, it is argued that the blur discomfort is strictly related to a loss of this information. Following this concept, a receptive field functional model, tuned to common features of natural scenes, is adopted to predict the visual discomfort. It is a complex-valued operator, orientation-selective both in the space domain and in the spatial frequency domain. Starting from the case of Gaussian blur, the analysis is extended to a generic type of blur by applying a positional Fisher Information equivalence criterion. Out-of-focus blur and astigmatic blur are presented as significant examples. The validity of the proposed model is verified by comparing its predictions with subjective ratings. The model fits linearly with the experiments reported in independent databases, based on different protocols and settings.