Quality Assessment of In-the-Wild Videos

Description

VSFA code for the following papers:

• Dingquan Li, Tingting Jiang, and Ming Jiang. Quality Assessment of In-the-Wild Videos. In Proceedings of the 27th ACM International Conference on Multimedia (MM ’19), October 21-25, 2019, Nice, France. [arxiv version]

Intra-Database Experiments (Training and Evaluating)

Feature extraction

CUDA_VISIBLE_DEVICES=0 python CNNfeatures.py --database=KoNViD-1k --frame_batch_size=64

You need to specify the database and change the corresponding videos_dir.

Quality prediction

CUDA_VISIBLE_DEVICES=0 python VSFA.py --database=KoNViD-1k --exp_id=0

You need to specify the database and exp_id.

Visualization

tensorboard --logdir=logs --port=6006 # in the server (host:port)
ssh -p port -L 6006:localhost:6006 user@host # in your PC. See the visualization in your PC

Reproduced results

We set seeds for the random generators and re-run the experiments on the same ten splits, i.e., the first 10 splits (exp_id=0~9). The results may be still not the same among different version of PyTorch. See randomness@Pytorch Docs

The reproduced overall results are better than the previous results published in the paper. We add learning rate scheduling in the updated code. Better hyper-parameters may be set, if you "look" at the training loss curve and the curves of validation results.

The mean (std) values of the first ten index splits (60%:20%:20% train:val:test)

	KoNViD-1k	CVD2014	LIVE-Qualcomm
SROCC	0.7728 (0.0189)	0.8698 (0.0368)	0.7726 (0.0611)
KROCC	0.5784 (0.0194)	0.6950 (0.0465)	0.5871 (0.0620)
PLCC	0.7754 (0.0192)	0.8678 (0.0315)	0.7954 (0.0553)
RMSE	0.4205 (0.0211)	10.8572 (1.3518)	7.5495 (0.7017)

Test Demo

The model weights provided in models/VSFA.pt are the saved weights when running the 9-th split of KoNViD-1k.

python test_demo.py --video_path=test.mp4

Requirement

conda create -n reproducibleresearch pip python=3.6
source activate reproducibleresearch
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
source deactive

• PyTorch 1.1.0
• TensorboardX 1.2, TensorFlow-TensorBoard

Note: The codes can also be directly run on PyTorch 1.3.

Contact

Dingquan Li, dingquanli AT pku DOT edu DOT cn.