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. IEEExplore(Open Access!) and [PCS2021] Efficient User-Generated Video Quality Prediction. IEEExplore. 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
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
- MATLAB >= 2019
- Deep learning toolbox (ResNet-50)
- Python3
- Sklearn
- FFmpeg
- Git LFS
| 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 |
|---|---|---|---|
 |  |  |  |
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 |
demo_compute_RAPIQUE_feats.m You need to specify the parameters
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 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 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} }