PyTorch Image Quality (PIQ) is a collection of measures and metrics for image quality assessment. PIQ helps you to concentrate on your experiments without the boilerplate code. The library contains a set of measures and metrics that is continually getting extended. For measures/metrics that can be used as loss functions, corresponding PyTorch modules are implemented.

We provide:

• Unified interface, which is easy to use and extend.
• Written on pure PyTorch with bare minima of additional dependencies.
• Extensive user input validation. Your code will not crash in the middle of the training.
• Fast (GPU computations available) and reliable.
• Most metrics can be backpropagated for model optimization.
• Supports python 3.7-3.10.

PIQ was initially named PhotoSynthesis.Metrics.

Installation

PyTorch Image Quality (PIQ) can be installed using pip, conda or git.

If you use pip, you can install it with:

$ pip install piq

If you use conda, you can install it with:

$ conda install piq -c photosynthesis-team -c conda-forge -c PyTorch

If you want to use the latest features straight from the master, clone PIQ repo:

git clone https://github.com/photosynthesis-team/piq.git
cd piq
python setup.py install

Documentation

The full documentation is available at .

Usage Examples

Image-Based metrics

The group of metrics (such as PSNR, SSIM, BRISQUE) takes an image or a pair of images as input to compute a distance between them. We have a functional interface, which returns a metric value, and a class interface, which allows to use any metric as a loss function.

import torch
from piq import ssim, SSIMLoss

x = torch.rand(4, 3, 256, 256, requires_grad=True)
y = torch.rand(4, 3, 256, 256)

ssim_index: torch.Tensor = ssim(x, y, data_range=1.)

loss = SSIMLoss(data_range=1.)
output: torch.Tensor = loss(x, y)
output.backward()

For a full list of examples, see image metrics examples.

Distribution-Based metrics

The group of metrics (such as IS, FID, KID) takes a list of image features to compute the distance between distributions. Image features can be extracted by some feature extractor network separately or by using the compute_feats method of a class.

Note:
compute_feats consumes a data loader of a predefined format.

import torch
from torch.utils.data import DataLoader
from piq import FID

first_dl, second_dl = DataLoader(), DataLoader()
fid_metric = FID()
first_feats = fid_metric.compute_feats(first_dl)
second_feats = fid_metric.compute_feats(second_dl)
fid: torch.Tensor = fid_metric(first_feats, second_feats)

If you already have image features, use the class interface for score computation:

import torch
from piq import FID

x_feats = torch.rand(10000, 1024)
y_feats = torch.rand(10000, 1024)
msid_metric = MSID()
msid: torch.Tensor = msid_metric(x_feats, y_feats)

For a full list of examples, see feature metrics examples.

List of metrics

Full-Reference (FR)

No-Reference (NR)

Distribution-Based (DB)

Benchmark

As part of our library we provide code to benchmark all metrics on a set of common Mean Opinon Scores databases. Currently we support several Full-Reference (TID2013, KADID10k and PIPAL) and No-Reference (KonIQ10k and LIVE-itW) datasets. You need to download them separately and provide path to images as an argument to the script.

Here is an example how to evaluate SSIM and MS-SSIM metrics on TID2013 dataset:

python3 tests/results_benchmark.py --dataset tid2013 --metrics SSIM MS-SSIM --path ~/datasets/tid2013 --batch_size 16

Below we provide a comparison between Spearman's Rank Correlation Coefficient (SRCC) values obtained with PIQ and reported in surveys. Closer SRCC values indicate the higher degree of agreement between results of computations on given datasets. We do not report Kendall rank correlation coefficient (KRCC) as it is highly correlated with SRCC and provides limited additional information. We do not report Pearson linear correlation coefficient (PLCC) as it's highly dependent on fitting method and is biased towards simple examples.

For metrics that can take greyscale or colour images, c means chromatic version.

Full-Reference (FR) Datasets

No-Reference (NR) Datasets

Unlike FR and NR IQMs, designed to compute an image-wise distance, the DB metrics compare distributions of sets of images. To address these problems, we adopt a different way of computing the DB IQMs proposed in . Instead of extracting features from the whole images, we crop them into overlapping tiles of size 96 × 96 with stride = 32. This pre-processing allows us to treat each pair of images as a pair of distributions of tiles, enabling further comparison. The other stages of computing the DB IQMs are kept intact.

Assertions

In PIQ we use assertions to raise meaningful messages when some component doesn't receive an input of the expected type. This makes prototyping and debugging easier, but it might hurt the performance. To disable all checks, use the Python -O flag: python -O your_script.py

Roadmap

See the open issues for a list of proposed features and known issues.

Contributing

If you would like to help develop this library, you'll find more information in our contribution guide.

Citation

If you use PIQ in your project, please, cite it as follows.

@misc{kastryulin2022piq,
  title = {PyTorch Image Quality: Metrics for Image Quality Assessment},
  url = {https://arxiv.org/abs/2208.14818},
  author = {Kastryulin, Sergey and Zakirov, Jamil and Prokopenko, Denis and Dylov, Dmitry V.},
  doi = {10.48550/ARXIV.2208.14818},
  publisher = {arXiv},
  year = {2022}
}

@misc{piq,
  title={{PyTorch Image Quality}: Metrics and Measure for Image Quality Assessment},
  url={https://github.com/photosynthesis-team/piq},
  note={Open-source software available at https://github.com/photosynthesis-team/piq},
  author={Sergey Kastryulin and Dzhamil Zakirov and Denis Prokopenko},
  year={2019}
}

Contacts

Sergey Kastryulin - @snk4tr - snk4tr@gmail.com

Jamil Zakirov - @zakajd - djamilzak@gmail.com

Denis Prokopenko - @denproc - d.prokopenko@outlook.com