VIDEVAL_release

A MATLAB implementation of feature fused VIDeo quality EVALuator (VIDEVAL) proposed in UGC-VQA: Benchmarking blind video quality assessment for user generated content.

Check out our performance benchmark results in https://github.com/tu184044109/BVQA_Benchmark.

Installation

• MATLAB
• FFmpeg
• python 3.6.7
• sklearn 0.20.3

Demos

Feature Extraction Only

demo_compute_VIDEVAL_feats.m

You need to specify the parameters

Quality Prediction with Pre-trained Model

This pre-trained model was trained on the combined dataset.

You need first extract features:

demo_compute_VIDEVAL_feats.m

Then run:

demo_pred_MOS_pretrained_VIDEVAL.py

Evaluation of BVQA Model on One Dataset

demo_eval_BVQA_feats_one_dataset.py

You need to specify the parameters

Evaluation of BVQA Model on All-Combined Dataset

demo_eval_BVQA_feats_all_combined.py

You need to specify the parameters

Citation

If you use this code for your research, please cite our papers.

@article{tu2020ugc,
  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={arXiv preprint arXiv:2005.14354},
  year={2020}
}

Contact

Zhengzhong TU, zhengzhong.tu@utexas.edu