An IQA toolbox with pure python and pytorch. Please refer to Awesome-Image-Quality-Assessment for a comprehensive survey of IQA methods and download links for IQA datasets.

This is a image quality assessment toolbox with pure python and pytorch. We provide reimplementation of many mainstream full reference (FR) and no reference (NR) metrics (results are calibrated with official matlab scripts if exist). With GPU acceleration, most of our implementations are much faster than Matlab. Please refer to the following documents for details:

• Dec 23, 2023. Add liqe and liqe_mix. Thanks for the contribution from Weixia Zhang 🤗.
• Oct 09, 2023. Add datasets: PIQ2023, GFIQA. Add metric topiq_nr-face. We release example results on FFHQ here for reference.
• Aug 15, 2023. Add st-lpips and laion_aes. Refer to official repo at ShiftTolerant-LPIPS and improved-aesthetic-predictor
• Aug 05, 2023. Add our work TOPIQ with remarkable performance on almost all benchmarks via efficient Resnet50 backbone. Use it with topiq_fr, topiq_nr, topiq_iaa for Full-Reference, No-Reference and Aesthetic assessment respectively.
• March 30, 2023. Add URanker for IQA of under water images.
• March 29, 2023. 🚨 Hot fix of NRQM & PI.
• March 25, 2023. Add TreS, HyperIQA, CNNIQA, CLIPIQA.
• More

• Ubuntu >= 18.04
• Python >= 3.8
• PyTorch >= 1.12
• Torchvision >= 0.13
• CUDA >= 10.2 (if use GPU)

# Install with pip
pip install pyiqa

# Install latest github version
pip uninstall pyiqa # if have older version installed already 
pip install git+https://github.com/chaofengc/IQA-PyTorch.git

# Install with git clone
git clone https://github.com/chaofengc/IQA-PyTorch.git
cd IQA-PyTorch
pip install -r requirements.txt
python setup.py develop

import pyiqa
import torch

# list all available metrics
print(pyiqa.list_models())

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

# create metric with default setting
iqa_metric = pyiqa.create_metric('lpips', device=device)
# Note that gradient propagation is disabled by default. set as_loss=True to enable it as a loss function.
# iqa_loss = pyiqa.create_metric('lpips', device=device, as_loss=True)

# create metric with custom setting
iqa_metric = pyiqa.create_metric('psnr', test_y_channel=True, color_space='ycbcr').to(device)

# check if lower better or higher better
print(iqa_metric.lower_better)

# example for iqa score inference
# Tensor inputs, img_tensor_x/y: (N, 3, H, W), RGB, 0 ~ 1
score_fr = iqa_metric(img_tensor_x, img_tensor_y)

# img path as inputs.
score_fr = iqa_metric('./ResultsCalibra/dist_dir/I03.bmp', './ResultsCalibra/ref_dir/I03.bmp')

# For FID metric, use directory or precomputed statistics as inputs
# refer to clean-fid for more details: https://github.com/GaParmar/clean-fid
fid_metric = pyiqa.create_metric('fid')
score = fid_metric('./ResultsCalibra/dist_dir/', './ResultsCalibra/ref_dir')
score = fid_metric('./ResultsCalibra/dist_dir/', dataset_name="FFHQ", dataset_res=1024, dataset_split="trainval70k")

Example test script with input directory/images and reference directory/images.

# example for FR metric with dirs
python inference_iqa.py -m LPIPS[or lpips] -i ./ResultsCalibra/dist_dir[dist_img] -r ./ResultsCalibra/ref_dir[ref_img]

# example for NR metric with single image
python inference_iqa.py -m brisque -i ./ResultsCalibra/dist_dir/I03.bmp

Please refer to the results calibration to verify the correctness of the python implementations compared with official scripts in matlab or python.

For convenience, we upload all related datasets to huggingface. Note that we only collect the datasets for academic, research, and educational purposes. It is important for the users to adhere to the usage guidelines, licensing terms, and conditions set forth by the original creators or owners of each dataset. Here are example codes to download datasets from huggingface:

import os
from huggingface_hub import snapshot_download

save_dir = './datasets'
os.makedirs(save_dir, exist_ok=True)

filename = "meta_info.tgz"
snapshot_download("chaofengc/IQA-Toolbox-Datasets", repo_type="dataset", local_dir=save_dir, allow_patterns=filename, local_dir_use_symlinks=False)

os.system(f"tar -xzvf {save_dir}/{filename} -C {save_dir}")

Examples to specific dataset options can be found in ./options/default_dataset_opt.yml. Details of the dataloader inferface and meta information files can be found in Dataset Preparation

We use official models for evaluation if available. Otherwise, we use the following settings to train and evaluate different models for simplicity and consistency:

Results are calculated with:

• PLCC without any correction. Although test time value correction is common in IQA papers, we want to use the original value in our benchmark.
• Full image single input. We do not use multi-patch testing unless necessary.

Basically, we use the largest existing datasets for training, and cross dataset evaluation performance for fair comparison. The following models do not provide official weights, and are retrained by our scripts:

Important Notes:

• Due to optimized training process, performance of some retrained approaches may be different with original paper.
• Results of all retrained models by ours are normalized to [0, 1] and change to higher better for convenience.
• Results of KonIQ-10k, AVA are both tested with official split.
• NIMA is only applicable to AVA dataset now. We use inception_resnet_v2 for default nima.
• MUSIQ is not included in the IAA benchmark because we do not have train/split information of the official model.

Here is an example script to get performance benchmark on different datasets:

# NOTE: this script will test ALL specified metrics on ALL specified datasets
# Test default metrics on default datasets
python benchmark_results.py -m psnr ssim -d csiq tid2013 tid2008

# Test with your own options
python benchmark_results.py -m psnr --data_opt options/example_benchmark_data_opts.yml

python benchmark_results.py --metric_opt options/example_benchmark_metric_opts.yml tid2013 tid2008

python benchmark_results.py --metric_opt options/example_benchmark_metric_opts.yml --data_opt options/example_benchmark_data_opts.yml

Example to train DBCNN on LIVEChallenge dataset

# train for single experiment
python pyiqa/train.py -opt options/train/DBCNN/train_DBCNN.yml

# train N splits for small datasets
python pyiqa/train_nsplits.py -opt options/train/DBCNN/train_DBCNN.yml

Any contributions to this repository are greatly appreciated. Please follow the contribution instructions for contribution guidance.

This work is licensed under a NTU S-Lab License and Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

If you find our codes helpful to your research, please consider to use the following citation:

@misc{pyiqa,
  title={{IQA-PyTorch}: PyTorch Toolbox for Image Quality Assessment},
  author={Chaofeng Chen and Jiadi Mo},
  year={2022},
  howpublished = "[Online]. Available: \url{https://github.com/chaofengc/IQA-PyTorch}"
}

Please also consider to cite our new work TOPIQ if it is useful to you:

@article{chen2023topiq,
  title={TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment},
  author={Chen, Chaofeng and Mo, Jiadi and Hou, Jingwen and Wu, Haoning and Liao, Liang and Sun, Wenxiu and Yan, Qiong and Lin, Weisi},
  journal={arXiv preprint arXiv:2308.03060},
  year={2023}
}

The code architecture is borrowed from BasicSR. Several implementations are taken from: IQA-optimization, Image-Quality-Assessment-Toolbox, piq, piqa, clean-fid

We also thanks the following public repositories: MUSIQ, DBCNN, NIMA, HyperIQA, CNNIQA, WaDIQaM, PieAPP, paq2piq, MANIQA

If you have any questions, please email chaofenghust@gmail.com