一个大规模、高质量的视频数据集Vimeo90K。这个数据集由从vimeo.com下载的8.98万个视频片段组成，其中包含大量场景和动作。它设计用于以下四个视频处理任务：时间帧插值、视频去噪、视频去块和视频超分辨率。                                                                                                                                                                                                                              

Vimeo90K数据集结构：

1、Triplet dataset (for temporal frame interpolation):
The triplet dataset consists of 73,171 3-frame sequences with a fixed resolution of 448 x 256, extracted from 15K selected video clips from Vimeo-90K. This dataset is designed for temporal frame interpolation. Download links are

• Testing set only (17GB): zip

• Both training and test set (33GB): zip

2、Septuplet dataset (for video denoising, deblocking, and super-resoluttion):
Notice: we have recently updated our testing denoising dataset to fix a bug in denoising test data generation. The new quantitative result of our algorithm is reported in our updated paper
The septuplet dataset consists of 91,701 7-frame sequences with fixed resolution 448 x 256, extracted from 39K selected video clips from Vimeo-90K. This dataset is designed to video denoising, deblocking, and super-resolution.

• The test set for video denoising (16GB): zip

• The test set for video deblocking (11GB): zip

• The test set for video super-resolution (6GB): zip

• The original test set (not downsampled or downgraded by noise) (15GB): zip

• The original training + test set (82GB): zip

Abstract

Many video processing algorithms rely on optical flow to register different frames within a sequence. However, a precise estimation of optical flow is often neither tractable nor optimal for a particular task. In this paper, we propose task-oriented flow (TOFlow), a flow representation tailored for specific video processing tasks. We design a neural network with a motion estimation component and a video processing component. These two parts can be jointly trained in a self-supervised manner to facilitate learning of the proposed TOFlow. We demonstrate that TOFlow outperforms the traditional optical flow on three different video processing tasks: frame interpolation, video denoising/deblocking, and video super-resolution. We also introduce Vimeo-90K, a large-scale, high-quality video dataset for video processing to better evaluate the proposed algorithm.

@article{xue2019video,
  title={Video Enhancement with Task-Oriented Flow},
  author={Xue, Tianfan and Chen, Baian and Wu, Jiajun and Wei, Donglai and Freeman, William T},
  journal={International Journal of Computer Vision (IJCV)},
  volume={127},
  number={8},
  pages={1106--1125},
  year={2019},
  publisher={Springer}
}