This paper presents a novel algorithm for assessing the motion stability of a video after stabilization. The assessment works in a non-reference manner that directly measures the intrinsic smoothness of the video motion path. Specifically, the motion path is cast as a curve embedded in the Lie group of homographies, and its smoothness is mathematically characterized by the intrinsic geodesic curvature. A bundle of paths are adopted to handle spatially variant motions through the frames. Then, we compute the weighted curvature for a holistic assessment on the motion stability. Other factors related to video stabilization, e.g., distortion and cropping, are also investigated as supplement. We collect 160 shaky video clips and their stabilized results for verification, and the experimental evidence shows the effectiveness of our algorithm in good correlation with human subjective judgements.

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.