piqa 1.1.0

PyTorch Image Quality Assessment

PIQA is not endorsed by Facebook, Inc.; PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.

PyTorch Image Quality Assessment

This package is a collection of measures and metrics for image quality assessment in various image processing tasks such as denoising, super-resolution, image interpolation, etc. It relies heavily on PyTorch and takes advantage of its efficiency and automatic differentiation.

It should be noted that piqa is directly inspired from the piq project, while focusing on the conciseness, readability and understandability of its (sub-)modules, such that anyone can freely and easily reuse and/or adapt them to its needs.

However, conciseness should never be at the expense of efficency; piqa's implementations are up to 2 times faster than those of other IQA PyTorch packages like kornia, piq and IQA-pytorch.

piqa should be pronounced pika (like Pikachu ⚡️)

Installation

The piqa package is available on PyPI, which means it is installable with pip:

pip install piqa

Alternatively, if you need the lastest features, you can install it using

git clone https://github.com/francois-rozet/piqa
cd piqa
python setup.py install

or copy the package directly to your project, with

git clone https://github.com/francois-rozet/piqa
cd piqa
cp -R piqa <path/to/project>/piqa

Getting started

The piqa package is divided in several submodules, each of which implements the functions and/or classes related to a specific image quality assessement metric.

import torch
from piqa import psnr, ssim

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

# PSNR function
l = psnr.psnr(x, y)

# SSIM instantiable object
criterion = ssim.SSIM().cuda()
l = criterion(x, y)
l.backward()

Metrics

Acronym	Module	Year	Metric
TV	piqa.tv	1937	Total Variation
PSNR	piqa.psnr	/	Peak Signal-to-Noise Ratio
SSIM	piqa.ssim	2004	Structural Similarity
MS-SSIM	piqa.ssim	2004	Multi-Scale Structural Similarity
LPIPS	piqa.lpips	2018	Learned Perceptual Image Patch Similarity
GMSD	piqa.gmsd	2013	Gradient Magnitude Similarity Deviation
MS-GMSD	piqa.gmsd	2017	Multi-Scale Gradient Magnitude Similiarity Deviation
MDSI	piqa.mdsi	2016	Mean Deviation Similarity Index
HaarPSI	piqa.haarpsi	2018	Haar Perceptual Similarity Index

Documentation

The documentation of this package is generated automatically using pdoc.

Code style

The code follows the Google Python style and is compliant with YAPF.