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- ### A Unified Interface for IQA Datasets
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- 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**.
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-
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- #### Citation
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- 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/).
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- If you find our project useful, please cite our paper
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- ```
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- @article{duanmu2021biqa,
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- author = {Duanmu, Zhengfang and Liu, Wentao and Wang, Zhongling and Wang, Zhou},
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- title = {Quantifying Visual Image Quality: A Bayesian View},
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- journal = {Annual Review of Vision Science},
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- volume = {7},
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- number = {1},
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- pages = {437-464},
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- year = {2021}
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- }
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- ```
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-
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- #### Supported Datasets
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-
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- | Dataset | Dis Img | Ref Img | MOS | DMOS |
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- | :-----------------------------------------------------------------------------------: | :----------------: | :----------------: | :----------------: | :----------------: |
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- | [LIVE](https://live.ece.utexas.edu/research/quality/subjective.htm) | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: |
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- | [A57](http://vision.eng.shizuoka.ac.jp/mod/page/view.php?id=26) | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: |
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- | [LIVE_MD](https://live.ece.utexas.edu/research/Quality/live_multidistortedimage.html) | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: |
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- | [MDID2013](https://ieeexplore.ieee.org/document/6879255) | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: |
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- | [CSIQ](http://vision.eng.shizuoka.ac.jp/mod/page/view.php?id=23) | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: |
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- | [KADID-10k](http://database.mmsp-kn.de/kadid-10k-database.html) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark:<sub>[(Note)](https://github.com/icbcbicc/IQA-Dataset/issues/3#issuecomment-2192649304)</sub> ~~~~| |
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- | [TID2008](http://www.ponomarenko.info/tid2008.htm) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
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- | [TID2013](http://www.ponomarenko.info/tid2013.htm) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
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- | [CIDIQ_MOS100](https://www.ntnu.edu/web/colourlab/software) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
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- | [CIDIQ_MOS50](https://www.ntnu.edu/web/colourlab/software) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
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- | [MDID2016](https://www.sciencedirect.com/science/article/abs/pii/S0031320316301911) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
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- | [SDIVL](http://www.ivl.disco.unimib.it/activities/imagequality/) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
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- | [MDIVL](http://www.ivl.disco.unimib.it/activities/imagequality/) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
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- | [Toyama](http://mict.eng.u-toyama.ac.jp/mictdb.html) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
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- | [PDAP-HDDS](https://sites.google.com/site/eelab907/zi-liao-ku) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
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- | [VCLFER](https://www.vcl.fer.hr/quality/vclfer.html) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
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- | [LIVE_Challenge](https://live.ece.utexas.edu/research/ChallengeDB/index.html) | :heavy_check_mark: | | :heavy_check_mark: | |
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- | [CID2013](https://zenodo.org/record/2647033#.YDSi73X0kUc) | :heavy_check_mark: | | :heavy_check_mark: | |
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- | [KonIQ-10k](http://database.mmsp-kn.de/koniq-10k-database.html) | :heavy_check_mark: | | :heavy_check_mark: | |
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- | [SPAQ](https://github.com/h4nwei/SPAQ) | :heavy_check_mark: | | :heavy_check_mark: | |
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- | [Waterloo_Exploration](https://ece.uwaterloo.ca/~k29ma/exploration/) | :heavy_check_mark: | :heavy_check_mark: | | |
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- | [<del>KADIS-700k</del>](http://database.mmsp-kn.de/kadid-10k-database.html) | :heavy_check_mark: <sub>(code only)</sub> | :heavy_check_mark: | | |
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- #### Basic Usage
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-
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- 0. Prerequisites
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- ```shell
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- pip install wget
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- ```
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- 1. General Python (please refer [```demo.py```](demo.py))
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- ```python
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- from load_dataset import load_dataset
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- dataset = load_dataset("LIVE")
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- ```
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- 2. PyTorch (please refer [```demo_pytorch.py```](demo_pytorch.py))
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- ```python
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- from load_dataset import load_dataset_pytorch
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- dataset = load_dataset_pytorch("LIVE")
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- ```
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-
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- #### Advanced Usage
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- 1. General Python (please refer [```demo.py```](demo.py))
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- ```python
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- from load_dataset import load_dataset
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- dataset = load_dataset("LIVE", dataset_root="data", attributes=["dis_img_path", "dis_type", "ref_img_path", "score"], download=True)
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- ```
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- 2. PyTorch (please refer [```demo_pytorch.py```](demo_pytorch.py))
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- ```python
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- from load_dataset import load_dataset_pytorch
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- transform = transforms.Compose([transforms.RandomCrop(size=64), transforms.ToTensor()])
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- dataset = load_dataset_pytorch("LIVE", dataset_root="data", attributes=["dis_img_path", "dis_type", "ref_img_path", "score"], download=True, transform=transform)
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- ```
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- #### TODO
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- - [ ] 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/)
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- - [ ] PyPI package
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- - [ ] HuggingFace dataset
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- - [ ] Provide more attributes
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- - [ ] ~~Add TensorFlow support~~
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- - [ ] ~~Add MATLAB support~~
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- #### Star History
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- [![Star History Chart](https://api.star-history.com/svg?repos=icbcbicc/IQA-Dataset&type=Date)](https://star-history.com/#icbcbicc/IQA-Dataset&Date)