Automatic detection of depth jump cuts and bent window effects in stereoscopic videos
3DTV and 3D cinema witness a significant increase in their popularity nowadays. New movie titles are released in 3D and there are more than 35 TV channels in various countries that broadcast in 3D worldwide. It is well known today and becomes more obvious, as the 3D video content availability increases, that stereoscopy is associated with certain 3D video quality issues that may affect in a negative way the 3D viewing experience. In this paper, we propose two novel algorithms that exploit available disparity information to detect two disturbing stereoscopic issues, namely depth jump cuts and bent window effects. Representative examples are provided to assess the algorithms performance. The proposed algorithms can be helpful in the post-production stage, where, in most cases, the detected issues can be fixed, and also in assessing the overall quality of stereoscopic video content.