Related papers: Perceptual Quality Assessment of UGC Gaming Videos

Perceptual Quality Assessment of UGC Gaming Videos

URL: http://arxiv.org/abs/2204.00128v1
Date: Thu, 31 Mar 2022 22:44:26 GMT
Title: Perceptual Quality Assessment of UGC Gaming Videos
Authors: Xiangxu Yu, Zhengzhong Tu, Neil Birkbeck, Yilin Wang, Balu Adsumilli and Alan C. Bovik
Abstract summary: We have created a new VQA model specifically designed to succeed on gaming videos. GAME-VQP successfully predicts the unique statistical characteristics of gaming videos. It both outperforms other mainstream general VQA models as well as VQA models specifically designed for gaming videos.
Score: 60.68777545735441
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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