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，通常由摄像头握持不稳导致。近年来虽涌现诸多视频稳像算法，但尚无专门且精确的指标能够全面评估视频稳定性。事实上，现有大多数质量评估模型将视频质量视为整体进行评价，未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确地衡量视频稳定性。此外，公开领域缺乏包含不同抖动程度视频及其对应主观评分的大规模数据库，这阻碍了面向稳定性的视频质量评估（VQA-S）的发展。为此，我们构建了名为StableDB的新数据库，包含1,952个具有不同程度抖动的UGC视频，每个视频均附有34名受试者评定的稳定程度平均意见得分（MOS）。同时，我们精心设计了新型VQA-S模型StableVQA，该模型包含三个特征提取器以分别获取光流、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，与现有VQA-S模型及通用VQA模型相比，StableVQA与主观意见的相关性更高。数据库与代码已开源至https://github.com/QMME/StableVQA。