https://github.com/bobholamovic/cnn-friqa
Convolutional Neural Network for Full-Reference color Image Quality Assessment
https://github.com/bobholamovic/cnn-friqa
cnn iqa pytorch
Last synced: 6 months ago
JSON representation
Convolutional Neural Network for Full-Reference color Image Quality Assessment
- Host: GitHub
- URL: https://github.com/bobholamovic/cnn-friqa
- Owner: Bobholamovic
- License: mit
- Created: 2018-12-03T05:09:51.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2019-06-03T04:29:12.000Z (over 6 years ago)
- Last Synced: 2025-03-25T12:50:52.612Z (7 months ago)
- Topics: cnn, iqa, pytorch
- Language: Python
- Homepage:
- Size: 61.5 KB
- Stars: 16
- Watchers: 1
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# CNN-FRIQA
Convolutional Neural Network for Full-Reference color Image Quality Assessment
This is a lightweight version of [deepIQA](https://arxiv.org/abs/1612.01697). The original project of deepIQA is [here](https://github.com/dmaniry/deepIQA) and the paper is [here](https://arxiv.org/abs/1612.01697).
## Environment and Dependencies
> Ubuntu 16.04 64-bit, Visual Studio Code, Python 3.5.2, Pytorch 0.4.0
`requirements.txt` is not included yet.
## Usage
### Data Preparation
Now the data lists are stored in `.json` files. The relative paths of the distorted images and the reference images to `data-dir` and the quality scores (ground-truth values) are contained in three arrays of a `json` object, with the fields specified as `img`, `ref`, and `score`, respectively. For example, `train_data.json` may look like this:
```
{
"img":
[
"distorted/img11_2_4.bmp",
"distorted/img6_3_3.bmp"
],
"ref":
[
"images/img11.bmp",
"distorted/img6.bmp"
],
"score":
[
0.5503,
0.4312
]
}
```
(this has been prettified as everthing actually on one line)
Also, there are `val_data.json` for validation subset and `test_data.json` for test subset. The lists are expected to be found at `list-dir`, which will be set to `data-dir` if not specified.
The scripts for data preparation on `Waterloo` and `TID2013` are provided.
### Running Code
Start from the root directory of this project,
```bash
cd src/
```
For training, try
```bash
python iqa.py train --resume pretrianed_model_path --data-dir DIR_OF_DATASET
```
If `pretrained_model_path` is not correctly specified, the model will learn from scratch.
Use
```bash
python iqa.py train --resume pretrianed_model_path | tee train.log
```
to dump logs.
For evaluation, try
```bash
python iqa.py train --evaluate --resume pretrained_model_path
```
For testing, try
```bash
python iqa.py test --resume pretrained_model_path
```
The code of testing the model on a single image is desired, yet to be provided.
As the patches are randomly extracted, there should be a random noise in the output of the model, which explains the slight difference of the performances upon different attempts.
Some pertrained models ~~and the script to make filename lists~~ are to be uploaded later.
## Experiment and Performance
Roughly, the `SROCC` value reaches `0.95` or higher under the best condition.
The experiment results are to be added here.
## Acknowledgement
+ The design of the model is based on [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://arxiv.org/abs/1612.01697)
+ Torch version of `MS-SSIM` from [lizhengwei1992/MS_SSIM_pytorch](https://github.com/lizhengwei1992/MS_SSIM_pytorch.git)
+ Part of the code layout from [fyu/drn](https://github.com/fyu/drn)
With best thanks!