[](https://paperswithcode.com/sota/video-quality-assessment-on-konvid-1k?p=rapique-rapid-and-accurate-video-quality)
[](https://paperswithcode.com/sota/video-quality-assessment-on-youtube-ugc?p=rapique-rapid-and-accurate-video-quality)
[](https://paperswithcode.com/sota/video-quality-assessment-on-live-vqc?p=rapique-rapid-and-accurate-video-quality)
# RAPIQUE
An official implementation of Rapid and Accurate Video Quality Evaluator (RAPIQUE) proposed in [IEEE OJSP2021] RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content. [Arxiv](https://arxiv.org/abs/2101.10955). [IEEExplore](https://ieeexplore.ieee.org/document/9463703)(Open Access!) and [PCS2021] Efficient User-Generated Video Quality Prediction. [IEEExplore](https://ieeexplore.ieee.org/abstract/document/9477483). Note that the temporal features can be used as standalone features in company with spatial models to boost performance on motion-intensive models. Check out the temporal-only modules in [ICIP21] A Temporal Statistics Model For UGC Video Quality Prediction. [IEEExplore](https://ieeexplore.ieee.org/abstract/document/9506669)
Check out our BVQA resource list and performance benchmark/leaderboard results in https://github.com/vztu/BVQA_Benchmark.
For more evaluation codes, please check out [VIDEVAL](https://github.com/vztu/VIDEVAL)
## Requirements
- MATLAB >= 2019
- Deep learning toolbox (ResNet-50)
- Python3
- Sklearn
- FFmpeg
- Git LFS
## Performances
### SRCC / PLCC
| Methods | KoNViD-1k | LIVE-VQC | YouTube-UGC | All-Combined |
|:------------:|:---------------------:|:--------------------:|:-------------------:|:------------:|
| TLVQM | 0.7101 / 0.7037 | 0.7988 / 0.8025 | 0.6693 / 0.6590 | 0.7271 / 0.7342 |
| VIDEVAL | 0.7832 / 0.7803 | 0.7522 / 0.7514 | 0.7787 / 0.7733 | 0.7960 / 0.7939 |
| MDVSFA | 0.7812 / 0.7856 | 0.7382 / 0.7728 | - / - | - / - |
| RAPIQUE | 0.8031 / 0.8175 | 0.7548 / 0.7863 | 0.7591 / 0.7684 | 0.8070 / 0.8229 |
Scatter plots and fitted logistic curves on these datasets:
| KonVid-1k | LIVE-VQC | YouTube-UGC | All-Combined |
|:-------------------------:|:-------------------------:|:-------:|:-------:|
| |  |  | 
### Speed
The unit is average `secs/video`.
| Methods | 540p | 720p | 1080p | 4k@60 |
|:-----------:|:----:|:----:|:------:|:--------:|
| Video-BLIINDS | 341.1 | 839.1 | 1989.9 | 16129.2 |
| VIDEVAL | 61.9 | 146.5 | 354.5 | 1716.3 |
| TLVQM | 34.5 | 78.9 | 183.8 | 969.3 |
| RAPIQUE | 13.5 | 17.3 | 18.3 | 112 |
### Performance vs. Speed
## Demos
#### Feature Extraction Only
```
demo_compute_RAPIQUE_feats.m
```
You need to specify the parameters
#### Evaluation of BVQA Model
We proposed several evaluation methods for BIQA/BVQA models. Please check out [ICASSP21] Regression or classification? New methods to evaluate no-reference picture and video quality models [IEEExplore](https://ieeexplore.ieee.org/abstract/document/9414232/) for details.
* For regression evaluation:
```
$ python evaluate_bvqa_features_regression.py
```
* For binary classification evaluation:
```
$ python evaluate_bvqa_features_binary_classification.py
```
* For ordinal classification evaluation:
```
$ python evaluate_bvqa_features_ordinal_classification.py
```
## Citation
If you use this code for your research, please cite our papers.
```
@article{tu2021rapique,
title={RAPIQUE: Rapid and accurate video quality prediction of user generated content},
author={Tu, Zhengzhong and Yu, Xiangxu and Wang, Yilin and Birkbeck, Neil and Adsumilli, Balu and Bovik, Alan C},
journal={IEEE Open Journal of Signal Processing},
volume={2},
pages={425--440},
year={2021},
publisher={IEEE}
}
@article{tu2021ugc,
title={UGC-VQA: Benchmarking blind video quality assessment for user generated content},
author={Tu, Zhengzhong and Wang, Yilin and Birkbeck, Neil and Adsumilli, Balu and Bovik, Alan C},
journal={IEEE Transactions on Image Processing},
year={2021},
publisher={IEEE}
}
@inproceedings{tu2021efficient,
title={Efficient User-Generated Video Quality Prediction},
author={Tu, Zhengzhong and Chen, Chia-Ju and Wang, Yilin and Birkbeck, Neil and Adsumilli, Balu and Bovik, Alan C},
booktitle={2021 Picture Coding Symposium (PCS)},
pages={1--5},
year={2021},
organization={IEEE}
}
@inproceedings{tu2021temporal,
title={A Temporal Statistics Model For UGC Video Quality Prediction},
author={Tu, Zhengzhong and Chen, Chia-Ju and Wang, Yilin and Birkbeck, Neil and Adsumilli, Balu and Bovik, Alan C},
booktitle={2021 IEEE International Conference on Image Processing (ICIP)},
pages={1454--1458},
year={2021},
organization={IEEE}
}
@inproceedings{tu2021regression,
title={Regression or classification? New methods to evaluate no-reference picture and video quality models},
author={Tu, Zhengzhong and Chen, Chia-Ju and Chen, Li-Heng and Wang, Yilin and Birkbeck, Neil and Adsumilli, Balu and Bovik, Alan C},
booktitle={ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={2085--2089},
year={2021},
organization={IEEE}
}
```