CVRKD-IQA(DistillationIQA)

This repository is for CVRKD-IQA introduced in the following paper

Guanghao Yin, Wei Wang, Zehuan Yuan, Chuchu Han, Wei Ji, Shouqian Sun and Changhu Wang, "Content-Variant Reference Image Quality Assessment via Knowledge Distillation", AAAI Oral, 2022 arxiv

Introduction

CVRKD-IQA is the first content-variant reference IQA method via knowledge distillation. The practicability of previous FR-IQAs is affected by the requirement for pixel-level aligned reference images. And NR-IQAs still have the potential to achieve better performance since HQ image information is not fully exploited. Hence, we use non-aligned reference (NAR) images to introduce various prior distributions of high-quality images. Moreover, the comparisons of distribution differences between HQ and LQ images can help our model better assess the image quality. Further, the knowledge distillation transfers more HQ-LQ distribution difference information from the FR-teacher to the NAR-student and stabilizing CVRKD-IQA performance. Since the content-variant and non-aligned reference HQ images are easy to obtain, our model can support more IQA applications with its relative robustness to content variations.

Prepare data

Training datasets

Download synthetic Kaddid-10K dataset. And download the training HQ images of DIV2K as the reference dataset.

Testing datasets

Download synthetic LIVE, CSIQ TID2013 and authentic KonIQ-10K datasets. And download the testing HQ images of DIV2K as the reference dataset.

Place those unzipped data in ./dataset file

Train

1.Train the FR-teacher

(1) (optional) Download models for our paper and place it in './model_zoo/' The models for FR-teacher can be downloaded from Google Cloud

(2) Quick start (you can change the options in option_train_DistillationIQA_FR.py)

Python train_DistillationIQA_FR.py --self_patch_num 10 --patch_size 224

2.Fix pretrained FR-teacher and train the NAR-student

(1) (optional) Download models for our paper and place it in './model_zoo/' The models for FR-teacher and NAR-student can be downloaded from Google Cloud

(2) Quick start (you can change the options in option_train_DistillationIQA.py)

Python train_DistillationIQA.py --self_patch_num 10 --patch_size 224

Test

(1) Make sure the trained models are placed in './model_zoo/FR_teacher_cross_dataset.pth' and './model_zoo/NAR_student_cross_dataset.pth'

(2) Quick start (you can change the options in option_train_DistillationIQA.py)

Python test_DistillationIQA.py

(3) test single image

Python test_DistillationIQA_single.py

More visual results

Synthetic examples of IQA scores predicted by our NAR-student when gradually increasing the distortion levels.

Real-data examples of IQA scores predicted by our NAR-student.

T-SNE visual visualization of HQ-LQ difference-aware features of NAR-student w/ and w/o KD

After distilled with FR-teacher, the HQ-LQ features in Fig(b) are clusterd. It proves that the HQ-LQ distribution difference prior from the FR-teacher can indeed help the NAR-student extract quality-sensitive discriminative features for more accurate and consistent performance.

Citation

If you find the code helpful in your resarch or work, please cite the following papers.

@article{yin2022content,
  title={Content-Variant Reference Image Quality Assessment via Knowledge Distillation},
  author={Yin, Guanghao and Wang, Wei and Yuan, Zehuan and Han, Chuchu and Ji, Wei and Sun, Shouqian and Wang, Changhu},
  journal={arXiv preprint arXiv:2202.13123},
  year={2022}
}

Acknowledgements

Part of our code is built on HyperIQA. We thank the authors for sharing their codes. Also thanks for the support of Bytedance.Inc