Extreme image or video completion, where, for instance, we only retain 1% of pixels in random locations, allows for very cheap sampling in terms of the required pre-processing. The consequence is, however, a reconstruction that is challenging for humans and inpainting algorithms alike. We propose an extension of a state-of-the-art extreme image completion algorithm to extreme video completion. We analyze a color-motion estimation approach based on color KL-divergence that is suitable for extremely sparse scenarios. Our algorithm leverages the estimate to adapt between its spatial and temporal filtering when reconstructing the sparse randomly-sampled video. We validate our results on 50 publicly-available videos using reconstruction PSNR and mean opinion scores.
@article{arxiv.2004.06409,
title = {Divergence-Based Adaptive Extreme Video Completion},
author = {Majed El Helou and Ruofan Zhou and Frank Schmutz and Fabrice Guibert and Sabine Süsstrunk},
journal= {arXiv preprint arXiv:2004.06409},
year = {2020}
}