Recently, Users Generated Content (UGC) videos becomes ubiquitous in our
daily lives. However, due to the limitations of photographic equipments and
techniques, UGC videos often contain various degradations, in which one of the
most visually unfavorable effects is the underexposure. Therefore,
corresponding video enhancement algorithms such as Low-Light Video Enhancement
(LLVE) have been proposed to deal with the specific degradation. However,
different from video enhancement algorithms, almost all existing Video Quality
Assessment (VQA) models are built generally rather than specifically, which
measure the quality of a video from a comprehensive perspective. To the best of
our knowledge, there is no VQA model specially designed for videos enhanced by
LLVE algorithms. To this end, we first construct a Low-Light Video Enhancement
Quality Assessment (LLVE-QA) dataset in which 254 original low-light videos are
collected and then enhanced by leveraging 8 LLVE algorithms to obtain 2,060
videos in total. Moreover, we propose a quality assessment model specialized in
LLVE, named Light-VQA. More concretely, since the brightness and noise have the
most impact on low-light enhanced VQA, we handcraft corresponding features and
integrate them with deep-learning-based semantic features as the overall
spatial information. As for temporal information, in addition to
deep-learning-based motion features, we also investigate the handcrafted
brightness consistency among video frames, and the overall temporal information
is their concatenation. Subsequently, spatial and temporal information is fused
to obtain the quality-aware representation of a video. Extensive experimental
results show that our Light-VQA achieves the best performance against the
current State-Of-The-Art (SOTA) on LLVE-QA and public dataset. Dataset and
Codes can be found at this https URL

通过构建 LLVE-QA 数据集并提出 Light-VQA 模型，本文针对 UGC 视频中的低照度造成视觉下降问题，同时考虑视觉质量评估方法，提供了一种基于特征融合的新型解决方案，该模型在 LLVE-QA 数据集和公共数据集上表现出最好的性能。