Three-Stream 3D deep CNN for no-Reference stereoscopic video quality assessment

•An end-to-end learning structure for the Stereoscopic Video Quality Assessment with three pipelines to extract the quality degradation from spatial, temporal, and disparity information.•A non-homogeneous data augmentation system that splits the stereoscopic video into small cube patches and removes the outliers from the created dataset using entropy criteria.•A robust deep depth quality-aware feature extractor used to extract motion and depth maps jointly and feed them synchronously to the deep network to extract quality discriminatory features.•Two popular datasets are used to validate the proposed learning architecture for Stereoscopic Video Quality Assessment and received state-of-the-art results in both datasets.