Metadata-Version: 2.1
Name: DeepDC_PyTorch
Version: 0.2
Summary: DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator
Home-page: https://github.com/h4nwei/DeepDC
Author: Hanwei ZHU
Author-email: hanwei.zhu@outlook.com
License: MIT
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](https://arxiv.org/abs/***). 
        
        ### Highlights:
        
        * A novel FR-IQA model that fully utilizes the *texture-sensitiv*e 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-PyTorch```
        
        ### Requirements: 
        - Python >= 3.6
        - PyTorch >= 1.0
        
        **Usage:** 
        ```python
        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
        
        ```bash
        git clone https://github.com/h4nwei/DeepDC
        cd DeepDC_PyTorch
        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
        ```bibtex
        @article{zhu2023DeepDC,
        title={DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator},
        author={Zhu, Hanwei and Chen, Baoliang and Zhu, Lingyu and Wang, Shiqi and Lin, Weisi},
        journal={CoRR},
        volume = {abs/2211.04927v2},
        year={2023},
        url = {https://arxiv.org/pdf/2211.04927v2.pdf}
        }
        
Keywords: pytorch,similarity,VGG,texture,structure,metric
Platform: python
Description-Content-Type: text/markdown
