Deep upscaling for video streaming : a case evaluation at SVT.

Abstract: While digital displays have continuously increased in resolution, video content produced before these improvements is however stuck at its original resolution, and the use of some form of scaling is needed for a satisfactory viewing experience on high-resolution displays. In recent years, the field of video scaling has taken a leap forward in output quality, due to the adoption of deep learning methods in research. In this paper, we describe a study wherein we train a convolutional neural network for super-resolution, and conduct a large-scale A/B video quality test in order to investigate if SVT video-ondemand viewers prefer video upscaled using a convolutional neural network to video upscaled using the standard bicubic method. Our results show that viewers generally prefer CNNscaled video, but not necessarily for the types of content this technology would primarily be used to scale. We conclude that the technology of deep upscaling shows promise, but also believe that more optimization and flexibility is need for deep scaling to be viable for mainstream use.