Automatic 3D defects identification in stereoscopic videos
3DTV and 3D cinema have become quite popular during the last few years. It is now well understood that certain 3D video quality issues may have a negative effect in the 3D viewing experience. In this paper, we propose two novel algorithms that exploit available disparity information, in order to detect two disturbing stereoscopic issues, namely Stereoscopic Window Violations (SWV) and bent window effects. The algorithms' performance is tested on a number of examples. The proposed algorithms can be used for assessing the overall quality of stereoscopic video content or in order to enable fixing the detected issues in a post-production stage.