A Retina-Based Perceptually Lossless Limit and a Gaussian Foveation Scheme With Loss Control

Abstract

Abstract-In this paper, we propose a new limit that promises theoretically achievable data reduction ratios up to approximately 9:1 with no perceptual loss in typical scenarios. Also, we introduce a novel Gaussian foveation scheme that provides experimentally achievable gains up to approximately 2 times the compression ratio of typical compression schemes with less perceptual loss than in typical transmissions. Both the proposed limit and foveation scheme shares the same background material: a model of image projection onto the retina; a model of cones distribution; and, subsequently, a proposed pointwise retina-based constraint called pixel efficiency. Quantitatively, the lattermost is globally processed to reveal the perceptual efficiency of a display. Analytical results indicate that in general the perceptual efficiency of displays are low for typical image sizes and viewing distances. Qualitatively, the pixel efficiency is used together with a lossy parameter to locally control the spatial resolution of a foveated image. Practical results show that proper use of the lossy parameter in the foveation filtering can increase the subjective quality of images.

Rapid saccadic categorization of other-race faces

“…This effect was not exaggerated compared with the full-spectrum conditions, as observed in a previous study ( Zhang et al, 2017 ). This discrepancy might be accounted for by the more ecologically valid retinal filter used in our study ( Targino Da Costa & Do, 2014 ), controlling for visual eccentricity. Overall, it is clear that race categorization saccadic responses were significantly faster than key press responses, even when the visual presentation paradigms were matched in terms of both visual quality and competition.…”

“…The stimuli and procedures used in study 2 were the same as in study 1; however, a high-pass spatial filter scaled by stimulus eccentricity (8.6° from the center of the face stimuli) was applied to the foveally presented stimuli to reconstruct the loss of visual acuity observed in extrafoveal vision ( Targino Da Costa & Do, 2014 ). The scaling of the retinal filter replicated the spatial frequencies available for processing in proportion to the distance from the fovea, where the furthest areas of the face stimuli contained lower spatial frequencies than those closer to the central visual field ( Figure 2 a).…”

Face-Information Sampling in Super-Recognizers

“…We used aperture sizes of 5°, 10°, 15°, 20°, and 25° of visual angle. To determine the preserved information in the Gaussian aperture, Papinutto and colleagues carried out a data-driven reconstruction of the Facespan based on the convolution of a retinal filter (Targino Da Costa & Do, 2014), computation of the Structural SIMilarity index (Wang et al, 2004) and use the pixel-test (Chauvin et al, 2005;based on Random Field Theory). Their analysis showed that a 17° Gaussian aperture corresponds to 7° or 45% of the total face information being preserved.…”

Face information sampling in super-recognizers

“…For the experiment, spotlight apertures were created in the same way as in Papinutto and colleagues (Papinutto et al, 2017) and we used aperture sizes of 5°, 10°, 15°, 20°, and 25° of visual angle. Papinutto and colleagues (Papinutto et al, 2017) also carried out a data-driven reconstruction of the Facespan based on the convolution of a retinal filter (Targino Da Costa & Do, 2014), computation of the Structural SIMilarity index (Wang et al, 2004) and use the pixel-test (Chauvin et al, 2005;based on Random Field Theory) to determine the preserved information in the Gaussian aperture. Their analysis showed that a 17° Gaussian aperture corresponds to 7° or 17% of the total face information being preserved.…”