Construction of a Video Inpainting Dataset Based on a Subjective Study

Video inpainting, the automated process of reconstructing missing or corrupted regions in video sequences, has gained significant attention in the fields of computer vision and image processing in recent years. However, a recurring remark in the literature has been the lack of a dedicated database specifically designed for video inpainting. As a result, existing inpainting studies have relied on locally created videos or datasets primarily intended for other applications. To address this limitation, this paper introduces the first publicly available video inpainting dataset accompanied by subjective scores, which closely aligns with real-world applications. The dataset covers three key inpainting scenarios: video hole completion, object removal, and post-stabilization inpainting. By providing this dataset, our goal is to facilitate the comparison and evaluation of both current and future video inpainting techniques. Moreover, we anticipate that it will serve as a solid foundation for the development of novel video inpainting assessment metrics in the future, thereby encouraging further advancements in this field. It is worth noting that, to the best of our knowledge, there is only one metric dedicated to video inpainting quality assessment, apart from those developed for image inpainting that can potentially be extended to videos. The dataset and the subjective scores are available on this link: https://github.com/rezkimed/VID-SS.