Perceptual Quality Assessment of UGC Gaming Videos

TL;DR

This paper introduces GAME-VQP, a specialized video quality assessment model for user-generated gaming videos, which outperforms existing general and gaming-specific VQA models on a large new database.

Contribution

The paper presents a novel VQA model tailored for UGC gaming videos, combining natural scene statistics features with gaming-specific CNN features.

Findings

GAME-VQP outperforms other VQA models on LIVE-YT-Gaming database.

The model effectively captures unique statistical characteristics of gaming videos.

Results demonstrate improved accuracy in predicting perceived video quality.

Abstract

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.