论文标题
UGC游戏视频的感知质量评估
Perceptual Quality Assessment of UGC Gaming Videos
论文作者
论文摘要
近年来，随着视频游戏行业的积极发展，诸如YouTube之类的主要视频网站上的游戏视频比例大大增加了。但是，对游戏视频的自动质量预测的研究相对较少，尤其是在属于“用户生成的内容”（UGC）类别的研究视频上。由于当前领先的通用视频质量评估（VQA）模型在此类游戏视频上的性能不佳，因此我们创建了一种新的VQA模型，专门设计用于在UGC游戏视频上取得成功，我们称之为游戏视频质量预测器（Game-vQP）。 Game-VQP通过利用在修改后的自然场景统计模型中设计的功能以及卷积神经网络学到的游戏特定功能，成功地预测了游戏视频的独特统计特征。我们在最近的一个名为Live-YT-Gaming的大型UGC游戏视频数据库中研究了游戏-VQP的性能，并发现它既优于其他主流通用VQA模型以及专门为游戏视频设计的VQA模型。接受纸张后，新模型将公开。
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.
