When enjoying video streaming services, users expect high video quality in various situations, including mobile phone connections with low bandwidths. Furthermore, the user's interest in consuming new large-size data content, such as high resolution/frame rate material or 360 degree videos, is gaining as well. To deal with such challenges, modern encoders adaptively reduce the size of the transmitted data. This in turn requires automated video quality monitoring solutions to ensure a sufficient quality of the material delivered. We present a no-reference video quality model; a model that does not require the original reference material, which is convenient for application in the field. Our approach uses a pretrained classification DNN in combination with hierarchical sub-image creation, some state-of-the-art features and a random forest model. Furthermore, the model can process UHD content and is trained on a large ground-truth data set, which is generated using a state-of-the-art full-reference model. The proposed model achieved a high quality prediction accuracy, comparable to a number of full-reference metrics. Thus our model is a proof-of-concept for a successful no-reference video quality estimation.
Steve Göring, Janto Skowronek, Alexander Raake, "DeViQ – A deep no reference video quality model" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Human Vision and Electronic Imaging, 2018, pp 1 - 6, https://doi.org/10.2352/ISSN.2470-1173.2018.14.HVEI-518