AI

Discontinuous Seam-Carving for Video Retargeting

Abstract

We introduce a new algorithm for video retargeting that uses discontinuous seam-carving in both space and time for resizing videos. We propose a novel appearance-based temporal coherence formulation that allows for frame-by-frame processing and results in temporally discontinuous seams, as opposed to geometrically smooth and continuous seams. This formulation optimizes the difference in appearance of the resultant retargeted frame to the optimal temporally coherent one, and allows for carving around fast moving salient regions. Additionally, we generalize the idea of appearance-based coherence to the spatial domain by introducing piece-wise spatial seams. Our spatial coherence measure minimizes the change in gradients during retargeting, which preserves spatial detail better than minimization of color difference alone. We also show that retargeting based on per-frame saliency (gradient-based or feature-based) does not always lead to desirable results and propose a novel automatically computed measure of spatio-temporal saliency. As needed, the user can also augment the saliency by interactive region-brushing. Our retargeting algorithm processes the video sequentially, which allows us to deal with streaming videos. We demonstrate results over a wide range of video examples and evaluate the effectiveness of each component of our algorithm.