The Vid3oC and IntVID Datasets for Video Super Resolution and Quality Mapping
Abstract
The current rapid advancements of computational hardware has opened the door for deep networks to be applied for real-time video processing, even on consumer devices. Appealing tasks include video super-resolution, compression artifact removal, and quality enhancement. These problems require high-quality datasets that can be applied for training and benchmarking. In this work, we therefore introduce two video datasets, aimed for a variety of tasks. First, we propose the Vid3oC dataset, containing 82 simultaneous recordings of 3 camera sensors. It is recorded with a multi-camera rig, including a high-quality DSLR camera, a high-end smartphone, and a stereo camera sensor. Second, we introduce the IntVID dataset, containing over 150 high-quality videos crawled from the internet. The datasets were employed for the AIM 2019 challenges for video super-resolution and quality mapping.
Cite
Text
Kim et al. "The Vid3oC and IntVID Datasets for Video Super Resolution and Quality Mapping." IEEE/CVF International Conference on Computer Vision Workshops, 2019. doi:10.1109/ICCVW.2019.00446Markdown
[Kim et al. "The Vid3oC and IntVID Datasets for Video Super Resolution and Quality Mapping." IEEE/CVF International Conference on Computer Vision Workshops, 2019.](https://mlanthology.org/iccvw/2019/kim2019iccvw-vid3oc/) doi:10.1109/ICCVW.2019.00446BibTeX
@inproceedings{kim2019iccvw-vid3oc,
title = {{The Vid3oC and IntVID Datasets for Video Super Resolution and Quality Mapping}},
author = {Kim, Sohyeong and Li, Guanju and Fuoli, Dario and Danelljan, Martin and Huang, Zhiwu and Gu, Shuhang and Timofte, Radu},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2019},
pages = {3609-3616},
doi = {10.1109/ICCVW.2019.00446},
url = {https://mlanthology.org/iccvw/2019/kim2019iccvw-vid3oc/}
}