This repository provides the official implementation of HiRQA, an opinion-unaware no-reference image quality assessment (NR-IQA) framework. HiRQA structures a hierarchical quality-aware embedding space using a novel pair-of-pairs ranking loss, embedding distance consistency loss, and contrastive alignment of image and text. It requires only a single distorted image during inference and generalizes effectively to real-world distortions. It also supports real-time quality prediction via its lightweight variant, HiRQA-S.
Paper:
HiRQA: Hierarchical Ranking and Quality Alignment for Opinion-Unaware Image Quality Assessment Vaishnav Ramesh, Haining Wang, Md Jahidul Islam
git clone https://github.com/uf-robopi/HiRQA.git
cd HiRQA
conda create -n hirqa python=3.10 -y
conda activate hirqa
conda install pytorch torchvision cudatoolkit=11.8 -c pytorch -c nvidia
pip install -r requirements.txtWe provide pretrained weights for:
- HiRQA (ResNet50))
- HiRQA-S (ResNet18)
You can download weights from Dropbox.
Use the --ckpt argument during inference.
To run HiRQA on benchmark datasets, first prepare the datasets as specified in their respective documentation. Then, provide the root directory as a parameter to the inference script.
python inference_iqa_dataset.py --dataset live --dataset_path /path/to/dataset/root --model HiRQA --ckpt /path/to/HiRQA.pthArguments:
--dataset: dataset name[ live, csiq, tid2013, kadid10k, livec, koniq10k, flive, spaq ]--dataset_path: Path to the root folder of benchmark dataset--model: Model backbone (HiRQAorHiRQA-S)--ckpt: Path to HiRQA or HiRQA-S.pthmodel
To run HiRQA on single image.
python single_image_inference.py --img_path /path/to/image --model HiRQA --ckpt /path/to/HiRQA.pthWe thank the authors of ARNIQA for their image degradation model and CLIP for their text encoder used during training for alignment. We also thank the contributors of KADIS-700k and other benchmark IQA datasets used in this work.