Characterizing distortions in first-person videos
Chen Bai, Amy R. Reibman · 2016
First-person videos (FPVs) captured by wearable cameras often contain heavy distortions, including motion blur, rolling shutter artifacts and rotation. Existing image and video quality estimators are inefficient for this type of video. We develop a method specifically to measure the distortions present in FPVs, without using a high quality reference video. Our local visual information (LVI) algorithm measures motion blur, and we combine homography estimation with line angle histogram to measure rolling shutter artifacts and rotation. Our experiments demonstrate that captured FPVs have dramatically different distortions compared to traditional source videos. We also show that LVI is responsive to motion blur, but insensitive to rotation and shear.