BVI-Artefact: An Artefact Detection Benchmark Dataset for Streamed Videos
This provides a benchmark for researchers and developers working on video streaming quality enhancement, though it is incremental as it focuses on dataset creation rather than a new detection method.
The authors tackled the lack of a comprehensive benchmark for detecting visual artefacts in streamed professionally generated content by creating BVI-Artefact, a dataset of 480 video sequences with binary labels for ten artefact types, and benchmarked existing methods, showing the task's difficulty and the need for more reliable detection.
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/