  The rising popularity of online User-Generated-Content (UGC) in the form of
streamed and shared videos, has hastened the development of perceptual Video
Quality Assessment (VQA) models, which can be used to help optimize their
delivery. Gaming videos, which are a relatively new type of UGC videos, are
created when skilled gamers post videos of their gameplay. These kinds of
screenshots of UGC gameplay videos have become extremely popular on major
streaming platforms like YouTube and Twitch. Synthetically-generated gaming
content presents challenges to existing VQA algorithms, including those based
on natural scene/video statistics models. Synthetically generated gaming
content presents different statistical behavior than naturalistic videos. A
number of studies have been directed towards understanding the perceptual
characteristics of professionally generated gaming videos arising in gaming
video streaming, online gaming, and cloud gaming. However, little work has been
done on understanding the quality of UGC gaming videos, and how it can be
characterized and predicted. Towards boosting the progress of gaming video VQA
model development, we conducted a comprehensive study of subjective and
objective VQA models on UGC gaming videos. To do this, we created a novel UGC
gaming video resource, called the LIVE-YouTube Gaming video quality
(LIVE-YT-Gaming) database, comprised of 600 real UGC gaming videos. We
conducted a subjective human study on this data, yielding 18,600 human quality
ratings recorded by 61 human subjects. We also evaluated a number of
state-of-the-art (SOTA) VQA models on the new database, including a new one,
called GAME-VQP, based on both natural video statistics and CNN-learned
features. To help support work in this field, we are making the new
LIVE-YT-Gaming Database, publicly available through the link:
https://live.ece.utexas.edu/research/LIVE-YT-Gaming/index.html .
