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Test Time Adaptation for Blind Image Quality Assessment (ICCV 2023)

This is the official project repository containing the implementation Code for Test Time Adaptation for Blind Image Quality Assessment (ICCV 2023) by Subhadeep Roy, Shankhanil Mitra, Soma Biswas, and Rajiv Soundararajan.

Our paper addresses the issue of the distribution shift across various image-quality databases and proposes methods to adapt the pre-trained model at test time in the absence of source data. The source model needs to be updated based on a self-supervised auxiliary task to learn the distribution shift between train and test data. We formulate novel self-supervised auxiliary tasks using the rank and group contrastive losses, which can learn quality-aware information from the test data. Block diagram of a general architecture for test time adaptation

Installation

conda env create -f environment.yml

Datasets

We have used mainly 6 datasets for evaluation ( KonIQ-10k , PIPAL , CID2013 , LIVE-IQA , SPAQ , LIVEC ).

Pretrained models

To download the pretrained model, run the following python scripts

TReS:

python3 TTA-IQA/TReS/weight/download_fblive.py

MUSIQ:

python3 TTA-IQA/MUSIQ/weights/download_fblive.py
python3 TTA-IQA/MUSIQ/model/download_fblive_resnet.py

HyperIQA:

python3 TTA-IQA/hyperIQA/weight/download_fblive.py

MetaIQA:

python3 TTA-IQA/MetaIQA/model_IQA/download.py

Run Code for Four Different Models

TReS:

python3 TTA-IQA/TReS/ALL_EXPT.py

MUSIQ:

python3 TTA-IQA/MUSIQ/ALL_EXPT.py

HyperIQA:

python3 TTA-IQA/hyperIQA/ALL_EXPT.py

MetaIQA:

python3 TTA-IQA/MetaIQA/ALL_EXPT.py

Generalize for any model

You need to write a code for developing the model. In the given demo, we write a code in TTA-IQA\General TTA\models.py to design the architecture of TReS model.Then one need to specify where model should be classified into feature extractor and regressor ( In our case, two examples are shown. If we do not include transformer as a part of feature extractor then we will use SSHead.py, otherwise we will use SSHead_tf.py. Rest all the codes are same. Just need to look up whether you have a csv file along with names of the images in 'image_name' column and their corresponding MOS score in 'MOS' column. After providing all these codes, you can run tta_inference.py . For example -

python3 TTA-IQA/General TTA/tta_inference.py --run 3 --batch_size 8 --lr 0.001 --niter 3 --svpath TTA-IQA/ttt_cifar_IQA/weight/fblive_TReS --gpunum 0 --test_patch_num 1 --fix_ssh --datapath DSLR --rank --group_contrastive

Acknowledgement

The main code for the model TReS, MUSIQ , HyperIQA , MetaIQA is borrowed from TReS, MUSIQ, hyperIQA, MetaIQA respectively.

Citation

@InProceedings{Roy_2023_ICCV,
    author    = {Roy, Subhadeep and Mitra, Shankhanil and Biswas, Soma and Soundararajan, Rajiv},
    title     = {Test Time Adaptation for Blind Image Quality Assessment},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {16742-16751}
}