In this paper we introduce two new no-reference metrics and compare their performance to state-of-the-art metrics on six publicly available datasets having a large variety of distortions and characteristics. Our two metrics, based on neural networks, combine the following features:
histogram of oriented gradients, edges detection, fast fourier transform, CPBD, blur and contrast measurement, temporal information, freeze detection, BRISQUE and Video BLIINDS. They perform better than Video BLIINDS and BRISQUE on the six datasets used in this study, including one made up
of natural videos that have not been artificially distorted. Our metrics show a good generalization as they achieved high performance on the six datasets.