Deep Visual Discomfort Predictor for Stereoscopic 3D Images

Suppressing effects of micro-vibration for MTF measurement of high-resolution electro-optical satellite payload in an optical alignment ground facility

Sensitivity to micro-vibration generated in an optical alignment ground facility increases with the optical aperture of the electro-optical payload mounted on the remote sensing satellite. The micro-vibration distorts the edge spread function (ESF) and changes the modulation transfer function (MTF) value during MTF measurements. Here, an ESF reconstruction network (ESFNet) is proposed to reconstruct the ESF distorted by micro-vibration. A proxy ground-truth ESF dataset for stable training of the proposed ESFNet via a supervised learning method is introduced. The proposed method is robust against both overfitting and underfitting for distorted parts of the ESF caused by micro-vibration. Experiments using ESF obtained in the facility with real micro-vibration show that the measurement error of MTF is around 0.02% when the ESFs are reconstructed using the proposed ESFNet.

Cybersickness and Its Severity Arising from Virtual Reality Content: A Comprehensive Study

Virtual reality (VR) experiences often elicit a negative effect, cybersickness, which results in nausea, disorientation, and visual discomfort. To quantitatively analyze the degree of cybersickness depending on various attributes of VR content (i.e., camera movement, field of view, path length, frame reference, and controllability), we generated cybersickness reference (CYRE) content with 52 VR scenes that represent different content attributes. A protocol for cybersickness evaluation was designed to collect subjective opinions from 154 participants as reliably as possible in conjunction with objective data such as rendered VR scenes and biological signals. By investigating the data obtained through the experiment, the statistically significant relationships—the degree that the cybersickness varies with each isolated content factor—are separately identified. We showed that the cybersickness severity was highly correlated with six biological features reflecting brain activities (i.e., relative power spectral densities of Fp1 delta, Fp 1 beta, Fp2 delta, Fp2 gamma, T4 delta, and T4 beta waves) with a coefficient of determination greater than 0.9. Moreover, our experimental results show that individual characteristics (age and susceptibility) are also quantitatively associated with cybersickness level. Notably, the constructed dataset contains a number of labels (i.e., subjective cybersickness scores) that correspond to each VR scene. We used these labels to build cybersickness prediction models and obtain a reliable predictive performance. Hence, the proposed dataset is supposed to be widely applicable in general-purpose scenarios regarding cybersickness quantification.