DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator
Project description
DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator
This is the repository of paper DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator.
Highlights:
- A novel FR-IQA model that fully utilizes the texture-sensitive of pre-trained DNN features, which computes distance correlation in the deep feature domain
- The model is exclusively based on the features of the pre-trained DNNs and does not rely on fine-tuning with MOSs
- Extensive experiments achieve superior performance on five standard IQA datasets, one perceptual similarity dataset, two texture similarity datasets, and one geometric transformation dataset.
- It can be employed as an objective function in texture synthesis and neural style transfer
====== Pytorch Implementation ======
Installation:
pip install DeepDC
Requirements:
- Python >= 3.6
- PyTorch >= 1.0
Usage:
from DeepDC_PyTorch import DeepDC
model = DeepDC()
# calculate DeepDC between X, Y (a batch of RGB images, data range: 0~1)
deepdc_score = model(X, Y)
or
git clone https://github.com/h4nwei/DeepDC
python DeepDC.py --ref <ref_path> --dist <dist_path>
Reference
- R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 586–595.
- K. Ding, K. Ma, S. Wang, and E. P. Simoncelli, “Image quality assessment: Unifying structure and texture similarity,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 5, pp. 2567–2581, 2020.
- I. Kligvasser, T. Shaham, Y. Bahat, and T. Michaeli, “Deep selfdissimilarities as powerful visual fingerprints,” in Neural Information Processing Systems, 2021, pp. 3939–3951.
Citation
@inproceedings{fang2020cvpr,
title={Perceptual Quality Assessment of Smartphone Photography},
author={Fang, Yuming and Zhu, Hanwei and Zeng, Yan and Ma, Kede and Wang, Zhou},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={3677-3686},
year={2020}
}
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