  In recent years, with the vigorous development of the video game industry,
the proportion of gaming videos on major video websites like YouTube has
dramatically increased. However, relatively little research has been done on
the automatic quality prediction of gaming videos, especially on those that
fall in the category of "User-Generated-Content" (UGC). Since current leading
general-purpose Video Quality Assessment (VQA) models do not perform well on
this type of gaming videos, we have created a new VQA model specifically
designed to succeed on UGC gaming videos, which we call the Gaming Video
Quality Predictor (GAME-VQP). GAME-VQP successfully predicts the unique
statistical characteristics of gaming videos by drawing upon features designed
under modified natural scene statistics models, combined with gaming specific
features learned by a Convolution Neural Network. We study the performance of
GAME-VQP on a very recent large UGC gaming video database called
LIVE-YT-Gaming, and find that it both outperforms other mainstream general VQA
models as well as VQA models specifically designed for gaming videos. The new
model will be made public after paper being accepted.
