  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/
