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，通常由相机手持不稳定所导致。近年来，众多视频稳定算法被提出，但尚无特定且精确的指标能够全面评估视频的稳定性。事实上，现有的大多数质量评估模型将视频质量作为一个整体进行评估，未能充分考虑视频稳定性的主观体验。因此，当存在严重抖动时，这些模型无法明确且精准地衡量视频稳定性。此外，公共领域缺乏包含不同程度抖动视频及其对应主观得分的规模化视频数据库，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，我们构建了一个名为StableDB的新数据库，其中包含1,952个不同程度抖动的UGC视频，每个视频均拥有由34名受试者评定的视频稳定性程度平均意见分（MOS）。同时，我们精心设计了一种名为StableVQA的新型VQA-S模型，该模型包含三个特征提取器，分别用于获取光流、语义和模糊特征，并通过回归层预测最终稳定性得分。大量实验表明，StableVQA在主观意见相关性上优于现有VQA-S模型及通用VQA模型。数据库与代码已开源至https://github.com/QMME/StableVQA。