Perceptual Quality Assessment of UGC Gaming Videos
This work addresses the need for better quality assessment in gaming videos on platforms like YouTube, which is an incremental improvement for video quality analysis in a specific domain.
The paper tackled the problem of automatic quality prediction for user-generated gaming videos, where existing general-purpose video quality assessment models perform poorly, and introduced GAME-VQP, a new model that outperforms other models on a large UGC gaming video database.
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.