Abstract
Video shakiness is a common problem for videos captured by hand-hold devices. How to evaluate the influence of video shakiness on human perception and design an objective quality assessment model is a challenging problem. In this work, we first conduct subjective experiments and construct a data-set with human scores. Then we extract a set of motion features related to video shakiness based on frequency analysis. Feature selection is applied on the extracted features and an objective model is learned based on the data-set. The experimental results show that the proposed model predicts video shakiness consistently with human perception and it can be applied to evaluating the existing video stabilization methods.
Cite
CITATION STYLE
Cui, Z., & Jiang, T. (2017). No-reference video shakiness quality assessment. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10115 LNCS, pp. 396–411). Springer Verlag. https://doi.org/10.1007/978-3-319-54193-8_25
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