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DVQA

(★ 482)

Deep learning-based Video Quality Assessment

파일 탐색기

최종 버전 다운로드 (.zip)

- - csiq_video_quality_data.txt
  - csiq_video_quality_seqs.txt
  - prep_csiq_score.py
- - live_video_quality_data.txt
  - live_video_quality_seqs.txt
  - prep_live_score.py
- - NFLX_dataset_public.py
  - prep_NFLX_score.py
- - videoset_subj_score_v2.json
- dataset.py
- network.py
- model_videoset_v3.pt
- eval.sh
- ft.sh
- train.sh
- decode_stream.py
- draw.py
.gitignore
CONTRIBUTING.md
eval.py
License.txt
opts.py
README.md
requirements.txt
train.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/Tencent/DVQA

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd DVQA

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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DVQA - Deep learning-based Video Quality Assessment

## News

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

## Installation

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.

## Dataset

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.

## Eval a dataset

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
```

## Train from scratch

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.

## Known issues

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.