IQA: Deep Image Structure and Texture Similarity Metric
This is the repository of paper Image Quality Assessment: Unifying Structure and Texture Similarity.
Three implementation versions:
DISTS_pt.py
(recommend)DISTS_tf.py
DISTS.m
.Installation:
pip install dists-pytorch
Usage:
from DISTS_pytorch import DISTS
D = DISTS()
# calculate DISTS between X, Y (a batch of RGB images, data range: 0~1)
# X: (N,C,H,W)
# Y: (N,C,H,W)
dists_value = D(X, Y)
# set 'require_grad=True, batch_average=True' to get a scalar value as loss.
dists_loss = D(X, Y, require_grad=True, batch_average=True)
dists_loss.backward()
or
git clone https://github.com/dingkeyan93/DISTS
cd DISTS_pytorch
python DISTS_pt.py --ref <ref_path> --dist <dist_path>
Requirements:
Usage:
git clone https://github.com/dingkeyan93/DISTS
cd DISTS_tensorflow
python DISTS_tf.py --ref <ref_path> --dist <dist_path>
Requirements:
Usage:
git clone https://github.com/dingkeyan93/DISTS
run demo.m
help DISTS
@article{ding2020iqa,
title={Image Quality Assessment: Unifying Structure and Texture Similarity},
author={Ding, Keyan and Ma, Kede and Wang, Shiqi and Simoncelli, Eero P.},
journal = {CoRR},
volume = {abs/2004.07728},
year={2020},
url = {https://arxiv.org/abs/2004.07728}
}