Awesome
Perceptual Optimization of Image Quality Assessment (IQA) Models
This repository re-implemented the existing IQA models with PyTorch, including
Note: The reproduced results may be a little different from the original matlab version.
Installation:
pip install IQA_pytorch
Requirements:
- Python>=3.6
- Pytorch>=1.2
Usage:
from IQA_pytorch import SSIM, GMSD, LPIPSvgg, DISTS
D = SSIM(channels=3)
# Calculate score of the image X with the reference Y
# X: (N,3,H,W)
# Y: (N,3,H,W)
# Tensor, data range: 0~1
score = D(X, Y, as_loss=False)
# set 'as_loss=True' to get a value as loss for optimizations.
loss = D(X, Y, as_loss=True)
loss.backward()
DNN-based optimization examples:
- Image denoising
- Blind image deblurring
- Single image super-resolution
- Lossy image compression
For the experiment results, please see Comparison of Image Quality Models for Optimization of Image Processing Systems
Citation:
@article{ding2020optim,
title={Comparison of Image Quality Models for Optimization of Image Processing Systems},
author={Ding, Keyan and Ma, Kede and Wang, Shiqi and Simoncelli, Eero P.},
journal = {CoRR},
volume = {abs/2005.01338},
year={2020},
url = {https://arxiv.org/abs/2005.01338}
}