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
this https URL

该研究旨在建立并验证一个大型数据库 BVI-Artefact，针对在线流媒体中的特定视觉瑕疵进行检测，同时使用此数据库对现有的检测方法进行评估，结果显示了这一任务的挑战性，并表明需要更可靠的瑕疵检测方法。

BVI-Artefact: 流媒体视频的伪影检测基准数据集

BVI-Artefact: An Artefact Detection Benchmark Dataset for Streamed  Videos

Nona-Bayer colour filter array (CFA) pattern is considered one of the most
viable alternatives to traditional Bayer patterns. Despite the substantial
advantages, such non-Bayer CFA patterns are susceptible to produce visual
artefacts while reconstructing RGB images from noisy sensor data. This study
addresses the challenges of learning RGB image reconstruction from noisy
Nona-Bayer CFA comprehensively. We propose a novel spatial-asymmetric attention
module to jointly learn bi-direction transformation and large-kernel global
attention to reduce the visual artefacts. We combine our proposed module with
adversarial learning to produce plausible images from Nona-Bayer CFA. The
feasibility of the proposed method has been verified and compared with the
state-of-the-art image reconstruction method. The experiments reveal that the
proposed method can reconstruct RGB images from noisy Nona-Bayer CFA without
producing any visually disturbing artefacts. Also, it can outperform the
state-of-the-art image reconstruction method in both qualitative and
quantitative comparison. Code available:
this https URL

本文介绍了如何使用新型空间 - 不对称注意力模块和对抗学习技术综合学习 Nona-Bayer CFA 噪声图像中的 RGB 图像重建，结果表明该方法在定性和定量比较上均优于现有的图像重建方法，并且可以避免产生任何视觉上妨碍的图像伪影。