  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.
