.. image:: https://raw.githubusercontent.com/photosynthesis-team/piq/master/docs/source/_static/piq_logo_main.png
:target: https://github.com/photosynthesis-team/piq

..

PyTorch Image Quality (PIQ) is not endorsed by Facebook, Inc.;

PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.

.. |pypy| image:: https://badge.fury.io/py/piq.svg
:target: https://pypi.org/project/piq/
:alt: Pypi Version
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:target: https://anaconda.org/photosynthesis-team/piq
:alt: Conda Version
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:alt: CI flake-8 style check
.. |tests| image:: https://github.com/photosynthesis-team/piq/workflows/testing/badge.svg
:alt: CI testing
.. |codecov| image:: https://codecov.io/gh/photosynthesis-team/piq/branch/master/graph/badge.svg
:target: https://codecov.io/gh/photosynthesis-team/piq
:alt: codecov
.. |quality_gate| image:: https://sonarcloud.io/api/project_badges/measure?project=photosynthesis-team_photosynthesis.metrics&metric=alert_status
:target: https://sonarcloud.io/dashboard?id=photosynthesis-team_photosynthesis.metrics
:alt: Quality Gate Status

.. intro-section-start

PyTorch Image Quality (PIQ) <https://github.com/photosynthesis-team/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 <https://pypi.org/project/photosynthesis-metrics/0.4.0/>_.

.. intro-section-end

.. installation-section-start

Installation

PyTorch Image Quality (PIQ) <https://github.com/photosynthesis-team/piq>_ can be installed using pip, conda or git.

If you use pip, you can install it with:

.. code-block:: sh

$ pip install piq

If you use conda, you can install it with:

.. code-block:: sh

$ 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 <https://github.com/photosynthesis-team/piq>_:

.. code-block:: sh

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

.. installation-section-end

.. documentation-section-start

Documentation

The full documentation is available at https://piq.readthedocs.io.

.. documentation-section-end

.. usage-examples-start

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.

.. code-block:: python

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 <https://github.com/photosynthesis-team/piq/blob/master/examples/image_metrics.py>_ 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.

.. code-block:: python

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:

.. code-block:: python

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 <https://github.com/photosynthesis-team/piq/blob/master/examples/feature_metrics.py>_ examples.

.. usage-examples-end

.. list-of-metrics-start

List of metrics

Full-Reference (FR)
^^^^^^^^^^^^^^^^^^^

=========== ====== ==========
Acronym Year Metric
=========== ====== ==========
PSNR - Peak Signal-to-Noise Ratio <https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio>_
SSIM 2003 Structural Similarity <https://en.wikipedia.org/wiki/Structural_similarity>_
MS-SSIM 2004 Multi-Scale Structural Similarity <https://ieeexplore.ieee.org/abstract/document/1292216>_
IW-SSIM 2011 Information Content Weighted Structural Similarity Index <https://ece.uwaterloo.ca/~z70wang/publications/IWSSIM.pdf>_
VIFp 2004 Visual Information Fidelity <https://ieeexplore.ieee.org/document/1576816>_
FSIM 2011 Feature Similarity Index Measure <https://ieeexplore.ieee.org/document/5705575>_
SR-SIM 2012 Spectral Residual Based Similarity <https://sse.tongji.edu.cn/linzhang/ICIP12/ICIP-SR-SIM.pdf>_
GMSD 2013 Gradient Magnitude Similarity Deviation <https://arxiv.org/abs/1308.3052>_
MS-GMSD 2017 Multi-Scale Gradient Magnitude Similarity Deviation <https://ieeexplore.ieee.org/document/7952357>_
VSI 2014 Visual Saliency-induced Index <https://ieeexplore.ieee.org/document/6873260>_
DSS 2015 DCT Subband Similarity Index <https://ieeexplore.ieee.org/document/7351172>_
- 2016 Content Score <https://arxiv.org/abs/1508.06576>_
- 2016 Style Score <https://arxiv.org/abs/1508.06576>_
HaarPSI 2016 Haar Perceptual Similarity Index <https://arxiv.org/abs/1607.06140>_
MDSI 2016 Mean Deviation Similarity Index <https://arxiv.org/abs/1608.07433>_
LPIPS 2018 Learned Perceptual Image Patch Similarity <https://arxiv.org/abs/1801.03924>_
PieAPP 2018 Perceptual Image-Error Assessment through Pairwise Preference <https://arxiv.org/abs/1806.02067>_
DISTS 2020 Deep Image Structure and Texture Similarity <https://arxiv.org/abs/2004.07728>_
=========== ====== ==========

No-Reference (NR)
^^^^^^^^^^^^^^^^^

=========== ====== ==========
Acronym Year Metric
=========== ====== ==========
TV 1937 Total Variation <https://en.wikipedia.org/wiki/Total_variation>_
BRISQUE 2012 Blind/Referenceless Image Spatial Quality Evaluator <https://ieeexplore.ieee.org/document/6272356>_
CLIP-IQA 2022 CLIP-IQA <https://arxiv.org/pdf/2207.12396.pdf>_
=========== ====== ==========

Distribution-Based (DB)
^^^^^^^^^^^^^^^^^^^^^^^

=========== ====== ==========
Acronym Year Metric
=========== ====== ==========
IS 2016 Inception Score <https://arxiv.org/abs/1606.03498>_
FID 2017 Frechet Inception Distance <https://arxiv.org/abs/1706.08500>_
GS 2018 Geometry Score <https://arxiv.org/abs/1802.02664>_
KID 2018 Kernel Inception Distance <https://arxiv.org/abs/1801.01401>_
MSID 2019 Multi-Scale Intrinsic Distance <https://arxiv.org/abs/1905.11141>_
PR 2019 Improved Precision and Recall <https://arxiv.org/abs/1904.06991>_
=========== ====== ==========

.. list-of-metrics-end

.. benchmark-section-start

Benchmark

As part of our library we provide code to benchmark <tests/results_benchmark.py>_ 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:

.. code-block:: bash

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 <https://en.wikipedia.org/wiki/Spearman%27s_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 <https://en.wikipedia.org/wiki/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 <https://en.wikipedia.org/wiki/Pearson_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
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
=========== =========================== =========================== ===========================
\ TID2013 KADID10k PIPAL

Source PIQ / Reference PIQ / Reference PIQ / Reference
=========== =========================== =========================== ===========================
PSNR 0.69 / 0.69 TID2013_ 0.68 / - 0.41 / 0.41 PIPAL_
SSIM 0.72 / 0.64 TID2013_ 0.72 / 0.72 KADID10k_ 0.50 / 0.53 PIPAL_
MS-SSIM 0.80 / 0.79 TID2013_ 0.80 / 0.80 KADID10k_ 0.55 / 0.46 PIPAL_
IW-SSIM 0.78 / 0.78 Eval2019_ 0.85 / 0.85 KADID10k_ 0.60 / -
VIFp 0.61 / 0.61 TID2013_ 0.65 / 0.65 KADID10k_ 0.50 / -
FSIM 0.80 / 0.80 TID2013_ 0.83 / 0.83 KADID10k_ 0.59 / 0.60 PIPAL_
FSIMc 0.85 / 0.85 TID2013_ 0.85 / 0.85 KADID10k_ 0.59 / -
SR-SIM 0.81 / 0.81 Eval2019_ 0.84 / 0.84 KADID10k_ 0.57 / -
SR-SIMc 0.87 / - 0.87 / - 0.57 / -
GMSD 0.80 / 0.80 MS-GMSD_ 0.85 / 0.85 KADID10k_ 0.58 / -
VSI 0.90 / 0.90 Eval2019_ 0.88 / 0.86 KADID10k_ 0.54 / -
DSS 0.79 / 0.79 Eval2019_ 0.86 / 0.86 KADID10k_ 0.63 / -
Content 0.71 / - 0.72 / - 0.45 / -
Style 0.54 / - 0.65 / - 0.34 / -
HaarPSI 0.87 / 0.87 HaarPSI_ 0.89 / 0.89 KADID10k_ 0.59 / -
MDSI 0.89 / 0.89 MDSI_ 0.89 / 0.89 KADID10k_ 0.59 / -
MS-GMSD 0.81 / 0.81 MS-GMSD_ 0.85 / - 0.59 / -
MS-GMSDc 0.89 / 0.89 MS-GMSD_ 0.87 / - 0.59 / -
LPIPS-VGG 0.67 / 0.67 DISTS_ 0.72 / - 0.57 / 0.58 PIPAL_
PieAPP 0.84 / 0.88 DISTS_ 0.87 / - 0.70 / 0.71 PIPAL_
DISTS 0.81 / 0.83 DISTS_ 0.88 / - 0.62 / 0.66 PIPAL_
BRISQUE 0.37 / 0.84 Eval2019_ 0.33 / 0.53 KADID10k_ 0.21 / -
CLIP-IQA 0.50 / - 0.48 / - 0.26 / -
IS 0.26 / - 0.25 / - 0.09 / -
FID 0.67 / - 0.66 / - 0.18 / -
KID 0.42 / - 0.66 / - 0.12 / -
MSID 0.21 / - 0.32 / - 0.01 / -
GS 0.37 / - 0.37 / - 0.02 / -
=========== =========================== =========================== ===========================

No-Reference (NR) Datasets
^^^^^^^^^^^^^^^^^^^^^^^^^^
=========== =========================== ===========================
\ KonIQ10k LIVE-itW

Source PIQ / Reference PIQ / Reference
=========== =========================== ===========================
BRISQUE 0.22 / - 0.31 / -
CLIP-IQA 0.68 / 0.68 CLIP-IQA off_ 0.64 / 0.64 CLIP-IQA off_
=========== =========================== ===========================

.. _TID2013: http://www.ponomarenko.info/tid2013.htm
.. _KADID10k: http://database.mmsp-kn.de/kadid-10k-database.html
.. _Eval2019: https://ieeexplore.ieee.org/abstract/document/8847307/
.. _MDSI: https://arxiv.org/abs/1608.07433
.. _MS-GMSD: https://ieeexplore.ieee.org/document/7952357
.. _DISTS: https://arxiv.org/abs/2004.07728
.. _HaarPSI: https://arxiv.org/abs/1607.06140
.. _PIPAL: https://arxiv.org/pdf/2011.15002.pdf
.. _IW-SSIM: https://ieeexplore.ieee.org/document/7442122
.. _KonIQ10k: http://database.mmsp-kn.de/koniq-10k-database.html
.. _LIVE-itW: https://live.ece.utexas.edu/research/ChallengeDB/index.html
.. _CLIP-IQA off: https://github.com/IceClear/CLIP-IQA

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 <https://arxiv.org/abs/2203.07809>_.
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.

.. benchmark-section-end

.. assertions-section-start

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

.. assertions-section-end

Roadmap

See the open issues <https://github.com/photosynthesis-team/piq/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 <CONTRIBUTING.rst>_.

.. citation-section-start

Citation

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

.. code-block:: tex

@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}
}

.. code-block:: tex

@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}
}

.. citation-section-end

.. contacts-section-start

Contacts

Sergey Kastryulin - @snk4tr <https://github.com/snk4tr>_ - snk4tr@gmail.com

Jamil Zakirov - @zakajd <https://github.com/zakajd>_ - djamilzak@gmail.com

Denis Prokopenko - @denproc <https://github.com/denproc>_ - d.prokopenko@outlook.com

.. contacts-section-end

Package profile

piq

Releases

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PyPI Downloads

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Details

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