由于诸如被动视频流（例如Twitch.tv）和云游戏（例如Nvidia Geforce Now）等新服务，视频游戏流服务正在迅速增长。与传统的视频内容相比，游戏内容具有一些特殊的特性，例如某些游戏的超高运动，特殊的运动模式，合成内容和重复性内容，这使得最新的视频和图像质量指标在此方面表现较弱。特殊计算机生成的内容。..

Towards Deep Learning Methods for Quality Assessment of Computer-Generated Imagery

Video gaming streaming services are growing rapidly due to new services such as passive video streaming, e.g. Twitch.tv, and cloud gaming, e.g. Nvidia Geforce Now. In contrast to traditional video content, gaming content has special characteristics such as extremely high motion for some games, special motion patterns, synthetic content and repetitive content, which makes the state-of-the-art video and image quality metrics perform weaker for this special computer generated content.In this paper, we outline our plan to build a deep learningbased quality metric for video gaming quality assessment. In addition, we present initial results by training the network based on VMAF values as a ground truth to give some insights on how to build a metric in future. The paper describes the method that is used to choose an appropriate Convolutional Neural Network architecture. Furthermore, we estimate the size of the required subjective quality dataset which achieves a sufficiently high performance. The results show that by taking around 5k images for training of the last six modules of Xception, we can obtain a relatively high performance metric to assess the quality of distorted video games.