README.md

---
license: mit
task_categories:
- video-classification
tags:
- video quality assessment
---

This is an **unofficial** copy of the videos in the *LSVQ dataset (Ying et al, CVPR, 2021)*, the largest dataset available for Non-reference Video Quality Assessment (NR-VQA); this is to facilitate research studies on this dataset given that we have received several reports that the original links of the dataset is not available anymore.

*See [FAST-VQA](https://github.com/VQAssessment/FAST-VQA-and-FasterVQA) (Wu et al, ECCV, 2022) or [DOVER](https://github.com/VQAssessment/DOVER) (Wu et al, ICCV, 2023) repo on its converted labels (i.e. quality scores for videos).*

The file links to the labels in either of the repositories above are as follows:

```
--- examplar_data_labels
--- --- train_labels.txt (this is the training set labels of LSVQ)
--- --- LSVQ
--- --- --- labels_test.txt (this is the LSVQ_test test subset)
--- --- --- labels_1080p.txt (this is the LSVQ_1080p test subset)
```

It should be noticed that the copyright of this dataset still belongs to the Facebook Research and LIVE Laboratory in UT Austin, and we may delete this unofficial repo at any time if requested by the copyright holders.

Here is the original copyright notice of this dataset, as follows.

-----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------ Copyright (c) 2020 The University of Texas at Austin All rights reserved.

Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, modify, and distribute this database (the images, the results and the source files) and its documentation for any purpose, provided that the copyright notice in its entirety appear in all copies of this database, and the original source of this database, Laboratory for Image and Video Engineering (LIVE, http://live.ece.utexas.edu ) at the University of Texas at Austin (UT Austin, http://www.utexas.edu ), is acknowledged in any publication that reports research using this database.

The following papers are to be cited in the bibliography whenever the database is used as:

Z. Ying, M. Mandal, D. Ghadiyaram and A.C. Bovik, "Patch-VQ: ‘Patching Up’ the Video Quality Problem," arXiv 2020.[paper]
Z. Ying, M. Mandal, D. Ghadiyaram and A.C. Bovik, "LIVE Large-Scale Social Video Quality (LSVQ) Database", Online:https://github.com/baidut/PatchVQ, 2020.
IN NO EVENT SHALL THE UNIVERSITY OF TEXAS AT AUSTIN BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OF THIS DATABASE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY OF TEXAS AT AUSTIN HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

THE UNIVERSITY OF TEXAS AT AUSTIN SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF TEXAS AT AUSTIN HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.

-----------COPYRIGHT NOTICE ENDS WITH THIS LINE------------