1 research outputs found
Photometric Stabilization for Fast-forward Videos
Videos captured by consumer cameras often exhibit temporal variations in
color and tone that are caused by camera auto-adjustments like white-balance
and exposure. When such videos are sub-sampled to play fast-forward, as in the
increasingly popular forms of timelapse and hyperlapse videos, these temporal
variations are exacerbated and appear as visually disturbing high frequency
flickering. Previous techniques to photometrically stabilize videos typically
rely on computing dense correspondences between video frames, and use these
correspondences to remove all color changes in the video sequences. However,
this approach is limited in fast-forward videos that often have large content
changes and also might exhibit changes in scene illumination that should be
preserved. In this work, we propose a novel photometric stabilization algorithm
for fast-forward videos that is robust to large content-variation across
frames. We compute pairwise color and tone transformations between neighboring
frames and smooth these pair-wise transformations while taking in account the
possibility of scene/content variations. This allows us to eliminate
high-frequency fluctuations, while still adapting to real variations in scene
characteristics. We evaluate our technique on a new dataset consisting of
controlled synthetic and real videos, and demonstrate that our techniques
outperforms the state-of-the-art.Comment: 9 pages, 11 figures, PG 201