Predicting blur visual discomfort for natural scenes by the loss of positional information. (December 2021)

• Record Type: Journal Article
• Authors: Di Claudio, Elio D.; Giannitrapani, Paolo; Jacovitti, Giovanni
• Is Part Of: Vision research. Volume 189(2021)
• Page Start: 33
• Page End: 45
• Publication Date: 2021-12
• Subjects: Visual perception -- Virtual receptive field -- Optical correction -- Image quality assessment; Vision -- Periodicals; 573.88
• DOI: 10.1016/j.visres.2021.07.018
• Languages: English
• ISSNs: 0042-6989

Abstract

Highlights: Discomfort by uncorrected refractive errors is proportional to detail localizability. Localizability is estimated by receptive fields sensing orientation and bandwidth. Blur discomfort is strictly correlated with the subjective quality loss of images. Abstract: The perception of 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 related 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 blur discomfort is strictly related to a loss of this information. Following this concept, a receptive field functional model 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.