DVQA - Deep learning-based Video Quality Assessment

• 12/17/2019 add pretrained model on PGC videos

We recommend to run the code with virtualenv. The code is developed with Python3.

Please install other prerequisites with the following command after invoking a virtual env.

pip install -r requirements.txt

All packages are required to run the code.

Please prepare a dataset if you want to evaluate in batch or train the code from scratch on your own GPUs. The dataset should be in json format, e.g. your_dataset.json

{
    "test": {
        "dis": ["dis_1.yuv", "dis_2.yuv"],
        "ref": ["ref_1.yuv", "ref_2.yuv"],
        "fps": [30, 24],
        "mos": [94.2, 55.8],
        "height": [1080, 720],
        "width": [1920, 1280]
    },
    "train": {
        "dis": ["dis_3.yuv", "dis_4.yuv"],
        "ref": ["ref_3.yuv", "ref_4.yuv"],
        "fps": [50, 24],
        "mos": [85.2, 51.8],
        "height": [320, 720],
        "width": [640, 1280]
    }
}

For the time being, only YUV is supported. We will update modules to read bitstream.

Put all YUV files (both dis and ref) in a folder and prepare your_dataset.json accordingly. Invoke virtualenv and run:

python eval.py --multi_gpu --video_dir /dir/to/yuv --score_file_path /path/to/your_dataset.json --load_model ./save/model_pgc.pt

Prepare dataset as above and simply run:

python train.py --multi_gpu --video_dir /dir/to/yuv --score_file_path /path/to/your_dataset.json --save_model ./save/your_new_trained.pt

Please check train.sh and opts.py if you would like to tweak other hyper-parameters.

The pretrained model was trained on 720P PGC videos compressed with H.264/AVC. It runs well with video of a resolution 1920x1080 and below.

We are not sure about the performance when the code is run with the following scenario,

1. PGC with other distortion types, especially time-related distortions.
2. PGC with post-processing filters, like de-nosing, super-resolution, artifacts reduction, etc.
3. UGC videos with pre-processing filter.
4. UGC videos compressed with common codecs.

We will try to answer above questions. Stay tuned.