Full-Reference Stability Assessment of Digital Video Stabilization Based on Riemannian Metric - Zhang, Lei (IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2018)
Assessing the quality of the motion stability is important to evaluating the performance of video stabilization algorithms. This paper presents a novel quality assessment scheme for the video motion stability in a full-reference (FR) manner. Given ideally stable videos and their corresponding shaky videos, our method measures the geodesic distance between motion paths of the stable and the stabilized videos. Due to the use of the Riemannian metric defined on the manifold of spatial transformations, our method enables the intrinsic and faithful measurement on pairwise motion disparities. To facilitate the FR assessment, a data set of stable and shaky videos is constructed by directly capturing realistic stable/shaky videos with a customized device. Then, digital video stabilization algorithms can be run on shaky videos to obtain the stabilized sequence of frames, whereupon their performances are evaluated by using our stability assessment. The experiments demonstrate that our stability assessment gains good concordance with the subjective assessment