Spatio-Temporal Measures Of Naturalness

Today, a wide variety of casual users of diverse videographic skills and styles capture a large portion of all videos, often equipped with uncertain hands and operating under difficult lighting conditions. These videos are taken with many types of camera devices having different characteristics, resulting in a wide range and diversity of video qualities. These are the kinds of videos shared on YouTube, Snapchat, and Face-book. Being able to predict the quality of these videos is an important goal for a variety of invested practitioners, including camera designers, cloud engineers, and users who could be directed to recapture videos of poor quality. In nearly every instance, a high quality reference video is not available, hence blind video quality predictors are of the greatest interest. Towards advancing this area, we have studied the spatiotemporal statistic of a wide variety of natural videos, constructed new directional temporal statistical models of videos, and studied whether measures of directional spatio-temporal naturalness can be developed that are predictive of quality.