The creation of high quality panoramic videos for immersive VR content is commonly done using a rig with multiple cameras covering the required scene. Unfortunately, this setup introduces both spatial and temporal artifacts due to the difference in optical centers as well as imperfect synchronization of cameras. Traditional image quality metrics are inadequate for describing geometric distortions in panoramic videos. In this paper, we propose an objective quality assessment approach for calculating these distortions based on optical flow coupled with an existing salience detection model. Our approach is validated with a human-centered study using error annotations and eye-tracking. Preliminary results indicate a good correlation between errors detected by the algorithm and human perception of errors.