The rising popularity of online User-Generated-Content (UGC) in the form of
streamed and shared videos, has hastened the development of perceptual Video
Quality Assessment (VQA) models, which can be used to help optimize their
delivery. Gaming videos, which are a relatively new type of UGC videos, are
created when skilled gamers post videos of their gameplay. These kinds of
screenshots of UGC gameplay videos have become extremely popular on major
streaming platforms like YouTube and Twitch. Synthetically-generated gaming
content presents challenges to existing VQA algorithms, including those based
on natural scene/video statistics models. Synthetically generated gaming
content presents different statistical behavior than naturalistic videos. A
number of studies have been directed towards understanding the perceptual
characteristics of professionally generated gaming videos arising in gaming
video streaming, online gaming, and cloud gaming. However, little work has been
done on understanding the quality of UGC gaming videos, and how it can be
characterized and predicted. Towards boosting the progress of gaming video VQA
model development, we conducted a comprehensive study of subjective and
objective VQA models on UGC gaming videos. To do this, we created a novel UGC
gaming video resource, called the LIVE-YouTube Gaming video quality
(LIVE-YT-Gaming) database, comprised of 600 real UGC gaming videos. We
conducted a subjective human study on this data, yielding 18,600 human quality
ratings recorded by 61 human subjects. We also evaluated a number of
state-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-learned
features. To help support work in this field, we are making the new
LIVE-YT-Gaming Database, publicly available through the link:
this https URL .

本文的研究旨在理解 UGC 游戏视频的感知特征，采用主观人类研究和客观评估方法评价零星的 UGC 游戏视频，评估了包括基于自然视频统计和 CNN 学习特征的 GAME-VQP 模型在内的多个最新 VQA 模型，为游戏视频的 VQA 模型开发提供帮助。

流媒体游戏视频的主观和客观分析

Subjective and Objective Analysis of Streamed Gaming Videos

No-reference (NR) perceptual video quality assessment (VQA) is a complex,
unsolved, and important problem to social and streaming media applications.
Efficient and accurate video quality predictors are needed to monitor and guide
the processing of billions of shared, often imperfect, user-generated content
(UGC). Unfortunately, current NR models are limited in their prediction
capabilities on real-world, "in-the-wild" UGC video data. To advance progress
on this problem, we created the largest (by far) subjective video quality
dataset, containing 39, 000 realworld distorted videos and 117, 000 space-time
localized video patches ('v-patches'), and 5.5M human perceptual quality
annotations. Using this, we created two unique NR-VQA models: (a) a
local-to-global region-based NR VQA architecture (called PVQ) that learns to
predict global video quality and achieves state-of-the-art performance on 3 UGC
datasets, and (b) a first-of-a-kind space-time video quality mapping engine
(called PVQ Mapper) that helps localize and visualize perceptual distortions in
space and time. We will make the new database and prediction models available
immediately following the review process.

该研究着重探讨了无参考视觉质量评估（NR VQA）问题，研究创建了包含 39,000 个失真视频和 117,000 个空时定位视频片段的最大人类感知质量评价数据库，以及两种独特的 NR-VQA 模型，并实现了在不同 UGC 数据集上的最先进性能，具有非常好的实践应用前景。