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 https://github.com/QMME/StableVQA.

翻译：视频抖动是用户生成内容（UGC）视频中常见的不良失真现象，通常由相机握持不稳导致。近年来，虽然已有诸多视频稳定算法被提出，但目前仍缺乏能够全面评估视频稳定性的专用精确指标。事实上，现有大多数质量评估模型将视频质量作为整体进行评价，未专门考虑用户对视频稳定性的主观体验。因此，当视频存在严重抖动时，这些模型无法明确且精确地测量视频稳定性。此外，公开领域尚缺乏包含不同抖动程度视频及其对应主观评分数值的大规模数据库，这阻碍了面向稳定性的视频质量评估（VQA-S）领域的发展。为此，我们构建了名为StableDB的新数据库，包含1,952个具有不同抖动程度的UGC视频，每个视频均包含由34名被试根据视频稳定性程度评定的平均意见分数（MOS）。进一步，我们精心设计了名为StableVQA的新型VQA-S模型，该模型包含三个特征提取器分别获取光流、语义和模糊特征，以及一个用于预测最终稳定性分数的回归层。大量实验表明，StableVQA与主观意见的相关性优于现有VQA-S模型及通用VQA模型。数据库和代码已开源至https://github.com/QMME/StableVQA。