Spatio-temporal segmentation based continuous no-reference stereoscopic video quality prediction

In this paper, we propose a no-reference continuous video quality prediction method for MPEG-2 MP@ML coded stereoscopic videos based on spatio-temporal segmentation. Segmented local features such as edge and non-edge areas based spatial artifacts, disparity, and temporal features are measures in this method. Blockiness and blur are considered to measure spatial artifacts for each stereo pair frames. A block based different zero-crossing approach is used for disparity measure. Each temporal segment is evaluated for spatial artifacts and disparity. In this method, temporal features are estimated separately for left and right video sequences based on segmented local features and sub temporal segment. Different weighting factors are then applied for the two different local features to measure the artifacts, disparity, and temporal features of a temporal segment. In order to verify the performance, we conducted subjective experiment on different symmetric and asymmetric coded stereo videos which indicates that our proposed method´s prediction performance is quite sufficient.