IQA-Dataset-team-IVC/IQA-Dataset

### A Unified Interface for IQA Datasets

This repository contains a unified interface for **downloading and loading** 20 popular Image Quality Assessment (IQA) datasets. We provide codes for both general **Python** and **PyTorch**.

#### Citation

This repository is part of our [Bayesian IQA project](http://ivc.uwaterloo.ca/research/bayesianIQA/) where we present an overview of IQA methods from a Bayesian perspective. More detailed summaries of both IQA models and datasets can be found in this [interactive webpage](http://ivc.uwaterloo.ca/research/bayesianIQA/).

If you find our project useful, please cite our paper
```
@article{duanmu2021biqa,
author = {Duanmu, Zhengfang and Liu, Wentao and Wang, Zhongling and Wang, Zhou},
title = {Quantifying Visual Image Quality: A Bayesian View},
journal = {Annual Review of Vision Science},
volume = {7},
number = {1},
pages = {437-464},
year = {2021}
}
```

#### Supported Datasets

| Dataset | Dis Img | Ref Img | MOS | DMOS |
| :-----------------------------------------------------------------------------------: | :----------------: | :----------------: | :----------------: | :----------------: |
| [LIVE](https://live.ece.utexas.edu/research/quality/subjective.htm) | ✓ | ✓ | | ✓ |
| [A57](http://vision.eng.shizuoka.ac.jp/mod/page/view.php?id=26) | ✓ | ✓ | | ✓ |
| [LIVE_MD](https://live.ece.utexas.edu/research/Quality/live_multidistortedimage.html) | ✓ | ✓ | | ✓ |
| [MDID2013](https://ieeexplore.ieee.org/document/6879255) | ✓ | ✓ | | ✓ |
| [CSIQ](http://vision.eng.shizuoka.ac.jp/mod/page/view.php?id=23) | ✓ | ✓ | | ✓ |
| [KADID-10k](http://database.mmsp-kn.de/kadid-10k-database.html) | ✓ | ✓ | ✓_{[(Note)](https://github.com/icbcbicc/IQA-Dataset/issues/3#issuecomment-2192649304)} ~~~~| |
| [TID2008](http://www.ponomarenko.info/tid2008.htm) | ✓ | ✓ | ✓ | |
| [TID2013](http://www.ponomarenko.info/tid2013.htm) | ✓ | ✓ | ✓ | |
| [CIDIQ_MOS100](https://www.ntnu.edu/web/colourlab/software) | ✓ | ✓ | ✓ | |
| [CIDIQ_MOS50](https://www.ntnu.edu/web/colourlab/software) | ✓ | ✓ | ✓ | |
| [MDID2016](https://www.sciencedirect.com/science/article/abs/pii/S0031320316301911) | ✓ | ✓ | ✓ | |
| [SDIVL](http://www.ivl.disco.unimib.it/activities/imagequality/) | ✓ | ✓ | ✓ | |
| [MDIVL](http://www.ivl.disco.unimib.it/activities/imagequality/) | ✓ | ✓ | ✓ | |
| [Toyama](http://mict.eng.u-toyama.ac.jp/mictdb.html) | ✓ | ✓ | ✓ | |
| [PDAP-HDDS](https://sites.google.com/site/eelab907/zi-liao-ku) | ✓ | ✓ | ✓ | |
| [VCLFER](https://www.vcl.fer.hr/quality/vclfer.html) | ✓ | ✓ | ✓ | |
| [LIVE_Challenge](https://live.ece.utexas.edu/research/ChallengeDB/index.html) | ✓ | | ✓ | |
| [CID2013](https://zenodo.org/record/2647033#.YDSi73X0kUc) | ✓ | | ✓ | |
| [KonIQ-10k](http://database.mmsp-kn.de/koniq-10k-database.html) | ✓ | | ✓ | |
| [SPAQ](https://github.com/h4nwei/SPAQ) | ✓ | | ✓ | |
| [Waterloo_Exploration](https://ece.uwaterloo.ca/~k29ma/exploration/) | ✓ | ✓ | | |
| [<del>KADIS-700k</del>](http://database.mmsp-kn.de/kadid-10k-database.html) | ✓ _{(code only)} | ✓ | | |

#### Basic Usage

0. Prerequisites
```shell
pip install wget
```

1. General Python (please refer [```demo.py```](demo.py))

```python
from load_dataset import load_dataset
dataset = load_dataset("LIVE")
```

2. PyTorch (please refer [```demo_pytorch.py```](demo_pytorch.py))

```python
from load_dataset import load_dataset_pytorch
dataset = load_dataset_pytorch("LIVE")
```

#### Advanced Usage

1. General Python (please refer [```demo.py```](demo.py))

```python
from load_dataset import load_dataset
dataset = load_dataset("LIVE", dataset_root="data", attributes=["dis_img_path", "dis_type", "ref_img_path", "score"], download=True)
```

2. PyTorch (please refer [```demo_pytorch.py```](demo_pytorch.py))

```python
from load_dataset import load_dataset_pytorch
transform = transforms.Compose([transforms.RandomCrop(size=64), transforms.ToTensor()])
dataset = load_dataset_pytorch("LIVE", dataset_root="data", attributes=["dis_img_path", "dis_type", "ref_img_path", "score"], download=True, transform=transform)
```

#### TODO

- [ ] Add more datasets: [PaQ-2-PiQ](https://github.com/baidut/PaQ-2-PiQ), [AVA](https://github.com/mtobeiyf/ava_downloader), [PIPAL](https://www.jasongt.com/projectpages/pipal.html), [AADB](https://github.com/aimerykong/deepImageAestheticsAnalysis), [FLIVE](https://github.com/niu-haoran/FLIVE_Database/blob/master/database_prep.ipynb), [BIQ2021](https://github.com/nisarahmedrana/BIQ2021), [IVC](http://ivc.univ-nantes.fr/en/databases/Subjective_Database/)
- [ ] PyPI package
- [ ] HuggingFace dataset
- [ ] Provide more attributes
- [ ] ~~Add TensorFlow support~~
- [ ] ~~Add MATLAB support~~

#### Star History

[](https://star-history.com/#icbcbicc/IQA-Dataset&Date)

### 图像质量评估（Image Quality Assessment, IQA）数据集统一接口

本仓库提供了针对20个主流图像质量评估（Image Quality Assessment, IQA）数据集的统一下载与加载接口，同时涵盖适配通用Python以及PyTorch的实现代码。

#### 引用说明
本仓库隶属于我们的[贝叶斯IQA项目](http://ivc.uwaterloo.ca/research/bayesianIQA/)，该项目从贝叶斯视角梳理了图像质量评估方法的研究脉络。关于IQA模型与数据集的更详细综述，可访问该[交互式网页](http://ivc.uwaterloo.ca/research/bayesianIQA/)查阅。
若您认为本项目对您的研究有所帮助，请引用我们的论文：

@article{duanmu2021biqa,
author = {Duanmu, Zhengfang and Liu, Wentao and Wang, Zhongling and Wang, Zhou},
title = {Quantifying Visual Image Quality: A Bayesian View},
journal = {Annual Review of Vision Science},
volume = {7},
number = {1},
pages = {437-464},
year = {2021}
}

#### 支持的数据集

| 数据集名称 | 失真图像 | 参考图像 | 主观平均分（Mean Opinion Score, MOS） | 差分主观平均分（Differential Mean Opinion Score, DMOS） |
| :-----------------------------------------------------------------------------------: | :----------------: | :----------------: | :----------------: | :----------------: |
| [LIVE](https://live.ece.utexas.edu/research/quality/subjective.htm) | ✓ | ✓ | | ✓ |
| [A57](http://vision.eng.shizuoka.ac.jp/mod/page/view.php?id=26) | ✓ | ✓ | | ✓ |
| [LIVE_MD](https://live.ece.utexas.edu/research/Quality/live_multidistortedimage.html) | ✓ | ✓ | | ✓ |
| [MDID2013](https://ieeexplore.ieee.org/document/6879255) | ✓ | ✓ | | ✓ |
| [CSIQ](http://vision.eng.shizuoka.ac.jp/mod/page/view.php?id=23) | ✓ | ✓ | | ✓ |
| [KADID-10k](http://database.mmsp-kn.de/kadid-10k-database.html) | ✓ | ✓ | ✓_{[(注)](https://github.com/icbcbicc/IQA-Dataset/issues/3#issuecomment-2192649304)} | |
| [TID2008](http://www.ponomarenko.info/tid2008.htm) | ✓ | ✓ | ✓ | |
| [TID2013](http://www.ponomarenko.info/tid2013.htm) | ✓ | ✓ | ✓ | |
| [CIDIQ_MOS100](https://www.ntnu.edu/web/colourlab/software) | ✓ | ✓ | ✓ | |
| [CIDIQ_MOS50](https://www.ntnu.edu/web/colourlab/software) | ✓ | ✓ | ✓ | |
| [MDID2016](https://www.sciencedirect.com/science/article/abs/pii/S0031320316301911) | ✓ | ✓ | ✓ | |
| [SDIVL](http://www.ivl.disco.unimib.it/activities/imagequality/) | ✓ | ✓ | ✓ | |
| [MDIVL](http://www.ivl.disco.unimib.it/activities/imagequality/) | ✓ | ✓ | ✓ | |
| [Toyama](http://mict.eng.u-toyama.ac.jp/mictdb.html) | ✓ | ✓ | ✓ | |
| [PDAP-HDDS](https://sites.google.com/site/eelab907/zi-liao-ku) | ✓ | ✓ | ✓ | |
| [VCLFER](https://www.vcl.fer.hr/quality/vclfer.html) | ✓ | ✓ | ✓ | |
| [LIVE_Challenge](https://live.ece.utexas.edu/research/ChallengeDB/index.html) | ✓ | | ✓ | |
| [CID2013](https://zenodo.org/record/2647033#.YDSi73X0kUc) | ✓ | | ✓ | |
| [KonIQ-10k](http://database.mmsp-kn.de/koniq-10k-database.html) | ✓ | | ✓ | |
| [SPAQ](https://github.com/h4nwei/SPAQ) | ✓ | | ✓ | |
| [Waterloo_Exploration](https://ece.uwaterloo.ca/~k29ma/exploration/) | ✓ | ✓ | | |
| [<del>KADIS-700k</del>](http://database.mmsp-kn.de/kadid-10k-database.html) | ✓ _{(仅支持代码加载)} | ✓ | | |

#### 基础使用方法

0. 前置依赖
shell
pip install wget

1. 通用Python版本（详见[`demo.py`](demo.py)）

python
from load_dataset import load_dataset
dataset = load_dataset("LIVE")

2. PyTorch版本（详见[`demo_pytorch.py`](demo_pytorch.py)）

python
from load_dataset import load_dataset_pytorch
dataset = load_dataset_pytorch("LIVE")

#### 高级使用方法

1. 通用Python版本（详见[`demo.py`](demo.py)）

python
from load_dataset import load_dataset
dataset = load_dataset("LIVE", dataset_root="data", attributes=["dis_img_path", "dis_type", "ref_img_path", "score"], download=True)

2. PyTorch版本（详见[`demo_pytorch.py`](demo_pytorch.py)）

python
from load_dataset import load_dataset_pytorch
transform = transforms.Compose([transforms.RandomCrop(size=64), transforms.ToTensor()])
dataset = load_dataset_pytorch("LIVE", dataset_root="data", attributes=["dis_img_path", "dis_type", "ref_img_path", "score"], download=True, transform=transform)

#### 待完成任务

- [ ] 新增更多数据集：[PaQ-2-PiQ](https://github.com/baidut/PaQ-2-PiQ)、[AVA](https://github.com/mtobeiyf/ava_downloader)、[PIPAL](https://www.jasongt.com/projectpages/pipal.html)、[AADB](https://github.com/aimerykong/deepImageAestheticsAnalysis)、[FLIVE](https://github.com/niu-haoran/FLIVE_Database/blob/master/database_prep.ipynb)、[BIQ2021](https://github.com/nisarahmedrana/BIQ2021)、[IVC](http://ivc.univ-nantes.fr/en/databases/Subjective_Database/)
- [ ] 发布PyPI软件包
- [ ] 支持Hugging Face数据集平台
- [ ] 提供更多数据集属性接口
- [ ] ~~新增TensorFlow支持~~
- [ ] ~~新增MATLAB支持~~

#### 星标历史

[](https://star-history.com/#icbcbicc/IQA-Dataset&Date)