Professionally generated content (PGC) streamed online can contain visual artefacts that degrade the quality of user experience. These artefacts arise from different stages of the streaming pipeline, including acquisition, post-production, compression, and transmission. To better guide streaming experience enhancement, it is important to detect specific artefacts at the user end in the absence of a pristine reference. In this work, we address the lack of a comprehensive benchmark for artefact detection within streamed PGC, via the creation and validation of a large database, BVI-Artefact. Considering the ten most relevant artefact types encountered in video streaming, we collected and generated 480 video sequences, each containing various artefacts with associated binary artefact labels. Based on this new database, existing artefact detection methods are benchmarked, with results showing the challenging nature of this tasks and indicating the requirement of more reliable artefact detection methods. To facilitate further research in this area, we have made BVI-Artifact publicly available at https://chenfeng-bristol.github.io/BVI-Artefact/

翻译：专业生成内容在在线流媒体中可能包含降低用户体验质量的视觉伪影。这些伪影源于流媒体管道中的不同阶段，包括采集、后期制作、压缩和传输。为更好地指导流媒体体验优化，在缺乏原始参考的情况下，检测用户端的特定伪影至关重要。本研究针对流媒体专业生成内容中缺乏综合伪影检测基准的问题，通过构建并验证大规模数据库BVI-Artefact予以解决。我们针对视频流中十种最相关的伪影类型，收集并生成了480个视频序列，每个序列包含多种伪影及其对应的二值伪影标签。基于该新数据库，我们对现有伪影检测方法进行了基准测试，结果表明该任务具有挑战性，且需要更可靠的伪影检测方法。为促进该领域的进一步研究，我们已在https://chenfeng-bristol.github.io/BVI-Artefact/ 公开提供BVI-Artefact数据集。