NRVQA
所属分类：视频捕捉采集剪辑
开发工具：Python
文件大小：0KB
下载次数：1
上传日期：2022-09-30 19:00:30
上 传 者：
sh-1993
说明： 无参考图像视频质量评估（BRISQUE NIQE PIQE DIQA deepBIQ VSFA
(no reference image video quaity assessment(BRISQUE NIQE PIQE DIQA deepBIQ VSFA)
文件列表:
DIQA/
VSFA/
__pycache__/
dataset/
deepbiq/
imgs/
__init__.py
brisque.py
niqe.py
niqe_image_params.mat
piqe.py
requirements.txt
sleeq.py
svr_brisque.joblib
test.py
train_brisque.py
# 无参考图片/视频质量评价(No-Reference Blind Video Quality Assessment)
## 深度学习
- [deepbiq](https://github.com/zhl2007/pytorch-image-quality-param-ctrl)
- [DIQA](https://towardsdatascience.com/deep-image-quality-assessment-with-tensorflow-2-0-69ed8c32f195)
- [VSFA](https://github.com/lidq92/VSFA)
## 传统方法
- BRISQUE(**extract 36 dimesion brisque features,you can train svr model in labeled datasets like TID2013/LIVE/CSIQ**)
- NIQE
- PIQE
## test
### brisque
*high score has high quality*
```
python test.py --mode brisque --path=imgs/origin.jpeg
python test.py --mode brisque --path=imgs/compression.jpeg
```
### niqe
*high score has low quality*
```
python test.py --mode niqe --path=imgs/origin.jpeg
python test.py --mode niqe --path=imgs/compression.jpeg
```
### piqe
*high score has low quality*
```
python test.py --mode piqe --path=imgs/origin.jpeg
python test.py --mode piqe --path=imgs/compression.jpeg
```
## 相关论文[reference papers](https://github.com/buyizhiyou/papers/tree/master/VQA_IQA)
## Thanks for your star!
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