[ICCV'21] Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment

Related tags

Deep Learning CKDN
Overview

CKDN

The official implementation of the ICCV2021 paper "Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment"

screenshot 173

Our trained model can be found in Model

The PIPAL dataset can be found in Here; our matched degraded images can be downloaded in Here. Please put all images into one folder.

To train the model, please run:

bash train.sh

To evaluate the model, please run:

bash val.sh

To predict the quality score for an image/folder, please:

  1. put degraded images into 'data_folder/degraded' and restored images into 'data_folder/restored' (with the same file name).
  2. run: bash predict_score.sh

Credits

This code is based on pytorch-image-models

Citation

@article{zheng2021learning,
  title={Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment},
  author={Zheng, Heliang and Fu, Jianlong and Zeng, Yanhong and Zha, Zheng-Jun and Luo, Jiebo},
  journal={ICCV},
  year={2021}
}
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Comments
  • Some early degraded-reference based work

    Some early degraded-reference based work

    In fact, some previous studies have proposed the use of degraded images for quality evaluation. [1] No-reference image contrast evaluation by generating bi-directional pseudo references [2] Tspr: Deep network-basedblind image quality assessment using two-side pseudo reference images

    opened by XiaoqiWang 0
  • reference images were also used during the testing phase???

    reference images were also used during the testing phase???

    Hi, as seen in the train.py and ckdn.py files, the test phase also used reference images that were inconsistent with the original paper description. Or did I miss the right?

    opened by XiaoqiWang 0
Owner
Multimedia Research
Multimedia Research at Microsoft Research Asia
Multimedia Research
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