A framework for computationally efficient video quality assessment

Objective video quality assessment (VQA) methods are essentially algorithms that estimate video quality. Recent quality assessment methods aim to provide quality predictions that are well correlated with subjective quality scores. However, most of these methods are computationally costly, which limits their use in real-time applications. A possible solution to this problem is to decrease the video resolution (spatial, temporal or both) in order to reduce the amount of processed data. Although reducing the video resolution is a simple way of decreasing the running time of a VQA method, this approach might impact the prediction accuracy of the VQA method. In this paper, we analyze this impact. More specifically, we analyze the effects of resolution reduction on the performance of the VQA methods. Based on this analysis, we propose a framework that decreases the overall processing time of VQA methods, without decreasing significantly the performance accuracy. We test the framework using six different VQA methods and four different video quality databases. Results show that the proposed framework reduces the average runtime performance of the tested VQA methods, without considerably altering their performance accuracy.