Estimating Uncertainty On Video Quality Metrics

Video Quality Metrics (VQM) are models used to predict the score that a user would give to the quality of a video visualization. They are widely used in video processing systems, for monitoring end-to-end quality or system troubleshooting for example. In these scenarios, the improvement is quantified based on a certain enhancement of a VQM score and trouble-detection is done based on a certain drop or threshold computed based on a VQM. Yet, whether such improvement or fault-detection is worth a significant increase in power consumption is questionable. Therefore, the goal of this work is to propose a method to predict the uncertainty of the quality metric. In this paper, we propose a framework to evaluate the confidence interval of a VQM for a given content using simple video features. We assess the performance of the framework by using the confidence intervals to predict if two videos are of similar or different quality and show that in most cases our approach performs better than just using a constant confidence interval.