Spatiotemporal Grid Flow for Video Retargeting
Video retargeting is a useful technique to adapt a video to a desired display resolution. It aims to preserve the information contained in the original video and the shapes of salient objects while maintaining the temporal coherence of contents in the video. Existing video retargeting schemes achieve temporal coherence via constraining each region/pixel to be deformed consistently with its corresponding region/pixel in neighboring frames. However, these methods often distort the shapes of salient objects, since they do not ensure the content consistency for regions/pixels constrained to be coherently deformed along time axis. In this paper, we propose a video retargeting scheme to simultaneously meet the two requirements. Our method first segments a video clip into spatiotemporal grids called grid flows, where the consistency of the content associated with a grid flow is maintained while retargeting the grid flow. After that, due to the coarse granularity of grid, there still may exist content inconsistency in some grid flows. We exploit the temporal redundancy in a grid flow to avoid that the grids with inconsistent content be incorrectly constrained to be coherently deformed. In particular, we use grid flows to select a set of key-frames which summarize a video clip, and resize subgrid-flows in these key-frames. We then resize the remaining nonkey-frames by simply interpolating their grid contents from the two nearest retargeted key-frames. With the key-frame-based scheme, we only need to solve a small-scale quadratic programming problem to resize subgrid-flows and perform grid interpolation, leading to low computation and memory costs. The experimental results demonstrate the superior performance of our scheme.