This repository contains a unified interface for downloading and loading 31 popular Image Quality Assessment (IQA) datasets. We provide codes for both general Python and PyTorch.
This repository is part of our Bayesian IQA project 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.
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}
}
Dataset | Dis Img | Ref Img | MOS | DMOS |
---|---|---|---|---|
LIVE | ✔️ | ✔️ | ✔️ | |
A57 | ✔️ | ✔️ | ✔️ | |
LIVE_MD | ✔️ | ✔️ | ✔️ | |
MDID2013 | ✔️ | ✔️ | ✔️ | |
CSIQ | ✔️ | ✔️ | ✔️ | |
KADID-10k | ✔️ | ✔️ | ✔️(Note) | |
TID2008 | ✔️ | ✔️ | ✔️ | |
TID2013 | ✔️ | ✔️ | ✔️ | |
CIDIQ_MOS100 | ✔️ | ✔️ | ✔️ | |
CIDIQ_MOS50 | ✔️ | ✔️ | ✔️ | |
MDID2016 | ✔️ | ✔️ | ✔️ | |
SDIVL | ✔️ | ✔️ | ✔️ | |
MDIVL | ✔️ | ✔️ | ✔️ | |
Toyama | ✔️ | ✔️ | ✔️ | |
PDAP-HDDS | ✔️ | ✔️ | ✔️ | |
VCLFER | ✔️ | ✔️ | ✔️ | |
PIPAL | ✔️ | ✔️ | ✔️ | |
LIVE_Challenge | ✔️ | ✔️ | ||
CID2013 | ✔️ | ✔️ | ||
KonIQ-10k | ✔️ | ✔️ | ||
SPAQ | ✔️ | ✔️ | ||
AADB | ✔️ | ✔️ | ||
BIQ2021 | ✔️ | ✔️ | ||
FLIVE | ✔️ | ✔️ | ||
GFIQA | ✔️ | ✔️ | ||
AVA | ✔️ | ✔️ | ||
PIQ2023 | ✔️ | ✔️ | ||
UHD-IQA | ✔️ | ✔️ | ||
Waterloo_Exploration | ✔️ | ✔️ | ||
BAPPS | ✔️ | ✔️ | 2AFC (no JND) | |
PieAPP | ✔️ | ✔️ | 2AFC | |
✔️ (code only) | ✔️ |
You can install this package in two ways:
-
Install from PyPI (recommended)
pip install iqadataset
-
Build from source (most updated)
git clone https://github.com/icbcbicc/IQA-Dataset.git cd IQA-Dataset pip install -e .
-
General Python (please refer
demo.py
)from iqadataset import load_dataset dataset = load_dataset("LIVE")
-
PyTorch (please refer
demo_pytorch.py
)from iqadataset import load_dataset_pytorch dataset = load_dataset_pytorch("LIVE")
-
General Python (please refer
demo.py
)from iqadataset import load_dataset dataset = load_dataset("LIVE", dataset_root="data", attributes=["dis_img_path", "dis_type", "ref_img_path", "score"], download=True)
-
PyTorch (please refer
demo_pytorch.py
)from iqadataset 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)