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Journal ArticleDOI

Perceptual Quality Assessment of UGC Gaming Videos

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TLDR
Zhang et al. as mentioned in this paper proposed a new VQA model called the Gaming Video Quality Predictor (GAME-VQP) to predict the unique statistical characteristics of gaming videos by drawing upon features designed under modified natural scene statistics models.
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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Citations
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Journal ArticleDOI

GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content

TL;DR: In this article , a new gaming-specific No-Reference Video Quality Assessment (NR VQA) model called the Gaming Video Quality Evaluator (GAMIVAL) was proposed, which combines and leverages the advantages of spatial and temporal gaming distorted scene statistics models, a neural noise model, and deep semantic features.
References
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Proceedings ArticleDOI

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Journal ArticleDOI

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Proceedings ArticleDOI

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Individual Comparisons by Ranking Methods

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Journal ArticleDOI

No-Reference Image Quality Assessment in the Spatial Domain

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