| ### 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) | ✓ | ✓ | ✓<sub>[(Note)](https://github.com/icbcbicc/IQA-Dataset/issues/3#issuecomment-2192649304)</sub> ~~~~| | | |
| | [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) | ✓ <sub>(code only)</sub> | ✓ | | | | |
| #### 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) |