Video shakiness is an unpleasant distortion of User Generated Content (UGC)
videos, which is usually caused by the unstable hold of cameras. In recent
years, many video stabilization algorithms have been proposed, yet no specific
and accurate metric enables comprehensively evaluating the stability of videos.
Indeed, most existing quality assessment models evaluate video quality as a
whole without specifically taking the subjective experience of video stability
into consideration. Therefore, these models cannot measure the video stability
explicitly and precisely when severe shakes are present. In addition, there is
no large-scale video database in public that includes various degrees of shaky
videos with the corresponding subjective scores available, which hinders the
development of Video Quality Assessment for Stability (VQA-S). To this end, we
build a new database named StableDB that contains 1,952 diversely-shaky UGC
videos, where each video has a Mean Opinion Score (MOS) on the degree of video
stability rated by 34 subjects. Moreover, we elaborately design a novel VQA-S
model named StableVQA, which consists of three feature extractors to acquire
the optical flow, semantic, and blur features respectively, and a regression
layer to predict the final stability score. Extensive experiments demonstrate
that the StableVQA achieves a higher correlation with subjective opinions than
the existing VQA-S models and generic VQA models. The database and codes are
available at this https URL

构建了一个新的数据库 StableDB，包含了 1,952 个多样性抖动的 UGC 视频，每个视频都有 34 位评审者对视频稳定度的评分，并通过设计了一个名为 StableVQA 的新型 VQA-S 模型，该模型包括三个特征提取器以获取光流、语义和模糊特征，并通过回归层预测最终的稳定得分。广泛实验证明，StableVQA 与主观意见的相关性高于现有的 VQA-S 模型和通用 VQA 模型。

StableVQA：一种基于深度学习的视频稳定性无参考质量评估模型

StableVQA: A Deep No-Reference Quality Assessment Model for Video  Stability

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。

Light-VQA: 低光视频增强的多维质量评估模型

Light-VQA: A Multi-Dimensional Quality Assessment Model for Low-Light  Video Enhancement

Recent years have witnessed an ever-expandingvolume of user-generated content
(UGC) videos available on the Internet. Nevertheless, progress on perceptual
quality assessmentof UGC videos still remains quite limited. There are many
distinguished characteristics of UGC videos in the complete video production
and delivery chain, and one important property closely relevant to video
quality is that there does not exist the pristine source after they are
uploaded to the hosting platform,such that they often undergo multiple
compression stages before ultimately viewed. To facilitate the UGC video
quality assessment,we created a UGC video perceptual quality assessment
database. It contains 50 source videos collected from TikTok with diverse
content, along with multiple distortion versions generated bythe compression
with different quantization levels and coding standards. Subjective quality
assessment was conducted to evaluate the video quality. Furthermore, we
benchmark the database using existing quality assessment algorithms, and
potential roomis observed to future improve the accuracy of UGC video quality
measures.

为了便于对 UGC 视频质量进行评估，我们创建了一个 UGC 视频质量评估数据库，其中包含 50 个源视频以及由不同量化级别和编码标准压缩生成的多个失真版本。我们进行了主观质量评估，并对数据库进行了基准测试，观察到未来改进 UGC 视频质量测量方法的潜在空间。