  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.
