ИСТИНА

Shooting video in 3D format can introduce stereoscopic artifacts, potentially causing viewers visual discomfort. In this work, we consider three common stereoscopic artifacts: color mismatch, sharpness mismatch, and geometric distortion. This paper introduces two neural-network-based methods for simultaneous color- and sharpness-mismatch estimation, as well as for estimating geometric distortions. To train these networks we prepared large datasets based on frames from full-length stereoscopic movies and compared the results with methods that previously served in analyses of full-length stereoscopic movies. We used our proposed methods to analyze 100 videos in VR180 format-a new format for stereoscopic videos in virtual reality (VR). This work presents overall results for these videos along with several examples of detected problems.