构建首个跨编码格式的游戏玩家视频质量评估数据集。
GameScope: A Multi-Attribute, Multi-Codec Benchmark Dataset for Gaming Video Quality Assessment
- 融合UGC与PGC内容，覆盖三种主流编码格式。
- 含4048段视频，每段平均37个主观评分，支持细粒度质量分析。
- 适合作为游戏视频质量评估模型的基准测试数据集。
游戏视频流媒体发展迅速，主流平台如YouTube和Twitch采用多种编码格式。为支持在任意编码下均表现稳定的质量评估模型，亟需大规模、多样化的主观游戏视频质量数据集。目前可用数据集数量有限且存在局限。为此，我们提出迄今最大的游戏视频质量数据集，涵盖用户生成内容（UGC）与专业生成内容（PGC），具有丰富的视觉多样性。数据集覆盖H.264、H.265和AV1三种最广泛使用的编码格式，包含4,048个视频样本，每个样本平均获得37个平均意见分（MOS）标注。除整体质量评分外，还收集了粗粒度质量属性，有助于深入理解感知因素。我们在该数据集上评估了主流视频质量评估方法的表现，其中一种视觉语言模型优于所有基准。据我们所知，这是首个全面覆盖多编码格式、多内容类型并包含质量属性的游戏视频质量评估数据集。数据集已公开：https://rajeshsureddi.github.io/GameScope/。
原文摘要 · Abstract (English)
The development of video game streaming has grown rapidly, with major platforms such as YouTube and Twitch using different codecs. To support quality assessment models that work consistently across any codec, it is necessary to have access to large, diverse subjective gaming quality datasets. Currently, there are only a few available, each having limitations. To address this gap, we present the largest gaming video quality dataset to date, incorporating both user-generated content (UGC) and professional-generated content (PGC) with extensive visual diversity. Our dataset covers the most widely used codecs - H.264, H.265, and AV1 - and consists of 4,048 video samples, each annotated by an average of 37 mean opinion score (MOS) ratings. In addition to overall quality scores, we collect coarse-grained quality attributes, enabling a better understanding of perceptual factors. We study the performance of leading video quality assessment methods on this dataset, including a vision language model that outperforms all the benchmarks. To the best of our knowledge, this is the first dataset that comprehensively addresses gaming video quality assessment across multiple codecs and content types with quality attributes. Our dataset is publicly available at https://rajeshsureddi.github.io/GameScope/.
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