Viewport-based CNN for visual quality assessment on 360° video.
Note that this is an updated version of the approach in our CVPR2019 paper, and thus the results are further improved. There are several differences between the CVPR2019 paper and this code.
Dataloader and the corresponding files for our VQA-ODV dataset are also provided.
At least 1 GPU is required by FlowNet2.
- python3
- PyTorch == 1.0.1 (CUDA 9.0 is required for compilation of FlowNet2)
- s2cnn: https://github.com/jonas-koehler/s2cnn
- FlowNet2: https://github.com/NVIDIA/flownet2-pytorch
- numpy
- scipy
- scikit-image
- tqdm
The binaries including pre-trained model, as well as the list files for VQA-ODV in inference can be obtained HERE.
Please put all these files under the log directory.
python test.py --log_dir /path/to/log/directory --flownet_ckpt /path/to/flownet2/pre-trained/model [--batch_size 1] [--num_workers 4] [--test_start_frame 21] [--test_interval 45]
Note that this released version only supports batch_size of 1 in inference. The num_workers should be set according to the condition of the computer.
It may spend a lot of time to test on all frames for each sequence. Therefore, frame drop can be set via test_start_frame and test_interval.
The default settings are to test every 45 frames for each sequence, beginning with the 22 frame.
If you find this code useful in your work, please acknowledge it appropriately and cite the paper:
@inproceedings{Li_2019_CVPR,
author = {Li, Chen and Xu, Mai and Jiang, Lai and Zhang, Shanyi and Tao, Xiaoming},
title = {Viewport Proposal CNN for 360deg Video Quality Assessment},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages = {10177--10186},
month = {June},
year = {2019}
}
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