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 https://github.com/wenzhouyidu/Light-VQA.

翻译：近年来，用户生成内容（UGC）视频在日常生活中变得无处不在。然而，由于摄影设备和技术的局限性，UGC视频常包含多种退化，其中视觉影响最不利的因素之一是曝光不足。因此，针对此类特定退化问题，研究者提出了低光照视频增强（LLVE）等视频增强算法。然而，与视频增强算法不同，现有的视频质量评估（VQA）模型几乎均为通用型而非专用型，即从综合视角评估视频质量。据我们所知，目前尚无专门为LLVE算法增强后的视频设计的VQA模型。为此，我们首先构建了低光照视频增强质量评估（LLVE-QA）数据集，其中收集了254个原始低光照视频，并利用8种LLVE算法进行增强，共获得2,060个视频。此外，我们提出了一种专门针对LLVE的质量评估模型，命名为Light-VQA。具体而言，由于亮度和噪声对低光照增强VQA影响最大，我们手工设计了相应特征，并将其与基于深度学习的语义特征整合，作为整体空间信息。在时间信息方面，除基于深度学习的运动特征外，我们还研究了视频帧间的手工亮度一致性特征，整体时间信息由两者拼接而成。随后，空间与时间信息被融合以得到视频的质量感知表征。大量实验结果表明，我们的Light-VQA在LLVE-QA数据集及公开数据集上均取得了优于当前最先进（SOTA）模型的性能。数据集与代码可访问https://github.com/wenzhouyidu/Light-VQA获取。