HyperIQA

This is the source code for the CVPR'20 paper "Blindly Assess Image Quality in the Wild Guided by A Self-Adaptive Hyper Network".

Dependencies

• Python 3.6+
• PyTorch 0.4+
• TorchVision
• scipy

(optional for loading specific IQA Datasets)

• csv (KonIQ-10k Dataset)
• openpyxl (BID Dataset)

Training & Testing

Training and testing our model on LIVE Challenge Dataset.

python train_test_IQA.py

Available options:

• --dataset: Training and testing dataset, support datasets: livec | koniq-10k | bid | live | csiq | tid2013.
• --train_patch_num: Sampled image patch number from each training image.
• --test_patch_num: Sampled image patch number from each testing image.
• --batch_size: Batch size.

Citation

If you find this work useful for your research, please cite our paper:

@InProceedings{Su_2020_CVPR,
author = {Su, Shaolin and Yan, Qingsen and Zhu, Yu and Zhang, Cheng and Ge, Xin and Sun, Jinqiu and Zhang, Yanning},
title = {Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}