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The rising popularity of online User-Generated-Content (UGC) in the form ofstreamed and shared videos, has hastened the development of perceptual VideoQuality Assessment (VQA) models, which can be used to help optimize theirdelivery. Gaming videos, which are a relatively new type of UGC videos, arecreated when skilled gamers post videos of their gameplay. These kinds ofscreenshots of UGC gameplay videos have become extremely popular on majorstreaming platforms like YouTube and Twitch. Synthetically-generated gamingcontent presents challenges to existing VQA algorithms, including those basedon natural scene/video statistics models. Synthetically generated gamingcontent presents different statistical behavior than naturalistic videos. Anumber of studies have been directed towards understanding the perceptualcharacteristics of professionally generated gaming videos arising in gamingvideo streaming, online gaming, and cloud gaming. However, little work has beendone on understanding the quality of UGC gaming videos, and how it can becharacterized and predicted. Towards boosting the progress of gaming video VQAmodel development, we conducted a comprehensive study of subjective andobjective VQA models on UGC gaming videos. To do this, we created a novel UGCgaming video resource, called the LIVE-YouTube Gaming video quality(LIVE-YT-Gaming) database, comprised of 600 real UGC gaming videos. Weconducted a subjective human study on this data, yielding 18,600 human qualityratings recorded by 61 human subjects. We also evaluated a number ofstate-of-the-art (SOTA) VQA models on the new database, including a new one,called GAME-VQP, based on both natural video statistics and CNN-learnedfeatures. To help support work in this field, we are making the newLIVE-YT-Gaming Database, publicly available through the link:https://live.ece.utexas.edu/research/LIVE-YT-Gaming/index.html .

Subjective and Objective Analysis of Streamed Gaming Videos