Age- and frequency-dependent changes in dynamic contrast perception in visual snow syndrome

Objective Patients with visual snow syndrome (VSS) suffer from a debilitating continuous (“TV noise-like”) visual disturbance. They report problems with vision at night and palinopsia despite normal visual acuity. The underlying pathophysiology of VSS is largely unknown. Currently, it is a clinical diagnosis based on the patient’s history, an objective test is not available. Here, we tested the hypothesis that patients with VSS have an increased threshold for detecting visual contrasts at particular temporal frequencies by measuring dynamic contrast detection-thresholds. Methods Twenty patients with VSS were compared to age-, gender-, migraine- and aura-matched controls in this case-control study. Subjects were shown bars randomly tilted to the left or right, flickering at six different frequencies (15 Hz, 20 Hz, 25 Hz, 30 Hz, 35 Hz, 40 Hz). The contrast threshold (CT) for detection of left or right tilt was measured in a two-alternative adaptive forced-choice procedure (QUEST). The threshold was defined as the Michelson contrast necessary to achieve the correct response in 75% of the cases. Results The CT increased for higher flicker frequencies (ANOVA: main effect frequency: F (5,180) = 942; p < 0.001), with an additional significant frequency*diagnosis interaction (ANOVA: F (5,180) = 5.00; p < 0.001). This interaction effect was due to an increased CT at a flicker frequency of 15 Hz in the VSS cohort (VSS: MC = 1.17%; controls: MC = 0.77%). At the other frequencies, group comparisons revealed no differences. Furthermore, in the VSS cohort we observed an increase of CT with higher age (r = 0.69; p < 0.001), which was not seen in controls (r = 0.30; p = 0.20). Conclusions This study demonstrates a lower visual contrast sensitivity exclusively at 15 Hz in VSS patients and demonstrates frequency-dependent differences in dynamic contrast vision. The peak sensitivities of both parvo- and magnocellular visual pathways are close to a frequency of about 10 Hz. Therefore, this frequency seems to be of crucial importance in everyday life. Thus, it seems plausible that the impairment of contrast sensitivity at 15 Hz might be an important pathophysiological correlate of VSS. Furthermore, the overall age-related decrease in contrast sensitivity only in VSS patients underscores the vulnerability of dynamic contrast detection in VSS patients. Dynamic CT detection seems to be a promising neurophysiological test that may contribute to the diagnosis of VSS.

Author Correction: Selective, high-contrast detection of syngeneic glioblastoma in vivo

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Kernel-phase analysis: Aperture modeling prescriptions that minimize calibration errors

Context. Kernel phase is a data analysis method based on a generalization of the notion of closure phase, which was invented in the context of interferometry, but it applies to well corrected diffraction dominated images produced by an arbitrary aperture. The linear model upon which it relies theoretically leads to the formation of observable quantities robust against residual aberrations. Aims. In practice, the detection limits that have been reported thus far seem to be dominated by systematic errors induced by calibration biases that were not sufficiently filtered out by the kernel projection operator. This paper focuses on the impact the initial modeling of the aperture has on these errors and introduces a strategy to mitigate them, using a more accurate aperture transmission model. Methods. The paper first uses idealized monochromatic simulations of a nontrivial aperture to illustrate the impact modeling choices have on calibration errors. It then applies the outlined prescription to two distinct data sets of images whose analysis has previously been published. Results. The use of a transmission model to describe the aperture results is a significant improvement over the previous type of analysis. The thus reprocessed data sets generally lead to more accurate results, which are less affected by systematic errors. Conclusions. As kernel-phase observing programs are becoming more ambitious, accuracy in the aperture description is becoming paramount to avoid situations where contrast detection limits are dominated by systematic errors. The prescriptions outlined in this paper will benefit from any attempt at exploiting kernel phase for high-contrast detection.

Mouse higher visual areas provide both distributed and discrete contributions to visually guided behaviors

SummaryCortical parallel processing streams segregate many diverse features of a sensory scene. However, some features are distributed across streams, begging the question of whether and how such distributed representations contribute to perception. We determined the necessity of primary visual cortex (V1) and three key higher visual areas (LM, AL and PM) for perception of orientation and contrast, two features that are robustly encoded across all four areas. Suppressing V1, LM or AL decreased sensitivity for both orientation discrimination and contrast detection, consistent with a role for these areas in sensory perception. In comparison, suppressing PM selectively increased false alarm rates during contrast detection, without any effect on orientation discrimination. This effect was not retinotopically-specific, suggesting a distinct role for PM in the regulation of noise during decision-making. Thus, we find that distributed representations in the visual system can nonetheless support specialized perceptual roles for higher visual cortical areas.

Fast Prediction of Contrast Detection Probability

Contrast detection probability (CDP) is proposed as an IEEE P2020 metric to predict camera performance intended for computer vision tasks for autonomous vehicles. Its calculation involves comparing combinations of pixel values between imaged patches. Computation of CDP for all meaningful combinations of m patches involves approximately 3/2(m2-m).n4 operations, where n is the length of one side of the patch in pixels. This work presents a method to estimate Weber contrast based CDP based on individual patch statistics and thus reduces to computation to approximately 4n2m calculations. For 180 patches of 10×10 pixels this is a reduction of approximately 6500 times and for 180 25×25 pixel patches, approximately 41000. The absolute error in the estimated CDP is less than 0.04 or 5% where the noise is well described by Gaussian statistics. Results are compared for simulated patches between the full calculation and the fast estimate. Basing the estimate of CDP on individual patch statistics, rather than by a pixel-to-pixel comparison facilitates the prediction of CDP values from a physical model of exposure and camera conditions. This allows Weber CDP behavior to be investigated for a wide variety of conditions and leads to the discovery that, for the case where contrast is increased by decreasing the tone value of one patch and therefore increasing noise as contrast increases, there exists a maxima which yields identical Weber CDP values for patches of different nominal contrast. This means Weber CDP is predicting the same detection performance for patches of different contrast.

Automotive Image Quality Concepts for the next SAE levels: Color Separation and Contrast Detection Probability

In this paper, we present an overview of automotive image quality challenges and link them to the physical properties of image acquisition. This process shows that the detection probability based KPIs are a helpful tool to link image quality to the tasks of the SAE classified supported and automated driving tasks. We develop questions around the challenges of the automotive image quality and show that especially color separation probability (CSP) and contrast detection probability (CDP) are a key enabler to improve the knowhow and overview of the image quality optimization problem. Next we introduce a proposal for color separation probability as a new KPI which is based on the random effects of photon shot noise and the properties of light spectra that cause color metamerism. This allows us to demonstrate the image quality influences related to color at different stages of the image generation pipeline. As a second part we investigated the already presented KPI Contrast Detection Probability and show how it links to different metrics of automotive imaging such as HDR, low light performance and detectivity of an object. As conclusion, this paper summarizes the status of the standardization status within IEEE P2020 of these detection probability based KPIs and outlines the next steps for these work packages.