Automatic Detection of 3D Quality Defects in Stereoscopic Videos Using Binocular Disparity

The 3D video quality issues that may disturb the human visual system and negatively impact the 3D viewing experience are well known and become more relevant as the availability of 3D video content increases, primarily through 3D cinema, but also through 3D television. In this paper, we propose four algorithms that exploit available stereo disparity information, in order to detect disturbing stereoscopic effects, namely, stereoscopic window violations, bent window effects, uncomfortable fusion object objects, and depth jump cuts on stereo videos. After detecting such issues, the proposed algorithms characterize them, based on the stress they cause to the viewer's visual system. Qualitative representative examples, quantitative experimental results on a custom-made video data set, a parameter sensitivity study, and comments on the computational complexity of the algorithms are provided, in order to assess the accuracy and the performance of stereoscopic quality defect detection.