Awesome Image Quality Assessment (IQA)
A comprehensive collection of IQA papers, datasets and codes. We also provide PyTorch implementations of mainstream metrics in IQA-PyTorch
Paper Link |
Method |
Type |
Published |
Code |
Keywords |
arXiv |
MANIQA |
NR |
CVPRW2022 |
Official |
Transformer, multi-dimension attention, dual branch |
arXiv |
TReS |
NR |
WACV2022 |
Official |
Transformer, relative ranking, self-consistency |
pdf |
KonIQ++ |
NR |
BMVC2021 |
Official |
Multi-task with distortion prediction |
arXiv |
MUSIQ |
NR |
ICCV2021 |
Official / Pytorch |
Multi-scale, transformer, Aspect Ratio Preserved (ARP) resizing |
arXiv |
CKDN |
NR |
ICCV2021 |
Official |
Degraded reference, Conditional knowledge distillation (related to HIQA) |
pdf |
HyperIQA |
NR |
CVPR2020 |
Official |
Content-aware hyper network |
arXiv |
Meta-IQA |
NR |
CVPR2020 |
Official |
Meta-learning |
arXiv |
GIQA |
NR |
ECCV2020 |
Official |
Generated image |
arXiv |
PI |
NR |
2018 PIRM Challenge |
Project |
1/2 * (NIQE + (10 - NRQM)). |
arXiv |
HIQA |
NR |
CVPR2018 |
Project |
Hallucinated reference |
arXiv |
BPSQM |
NR |
CVPR2018 |
|
Pixel-wise quality map |
arXiv |
RankIQA |
NR |
ICCV2017 |
Github |
Pretrain on synthetically ranked data |
pdf |
CNNIQA |
NR |
CVPR2014 |
PyTorch |
First CNN-based NR-IQA |
|
|
|
|
|
|
arXiv |
UNIQUE |
NR |
TIP2021 |
Github |
Combine synthetic and authentic image pairs |
arXiv |
DBCNN |
NR |
TCSVT2020 |
Official |
Two branches for synthetic and authentic distortions |
pdf |
SFA |
NR |
TMM2019 |
Official |
Aggregate ResNet50 features of multiple cropped patches |
pdf/arXiv |
PQR |
NR/Aesthetic |
TIP2019 |
Official1/Official2 |
Unify different type of aesthetic labels |
arXiv |
WaDIQaM (deepIQA) |
NR/FR |
TIP2018 |
PyTorch |
Weighted average of patch qualities, shared FR/NR models |
pdf |
NIMA |
NR |
TIP2018 |
PyTorch/Tensorflow |
Squared EMD loss |
pdf |
MEON |
NR |
TIP2017 |
|
Multi-task: distortion learning and quality prediction |
arXiv |
dipIQ |
NR |
TIP2017 |
download |
Similar to RankIQA |
|
|
|
|
|
|
arXiv |
NRQM (Ma) |
NR |
CVIU2017 |
Project |
Traditional, Super resolution |
arXiv |
FRIQUEE |
NR |
JoV2017 |
Official |
Authentically Distorted, Bag of Features |
IEEE |
HOSA |
NR |
TIP2016 |
Matlab download |
Traditional |
pdf |
ILNIQE |
NR |
TIP2015 |
Official |
Traditional |
pdf |
BRISQUE |
NR |
TIP2012 |
Official |
Traditional |
pdf |
BLIINDS-II |
NR |
TIP2012 |
Official |
|
pdf |
CORNIA |
NR |
CVPR2012 |
Matlab download |
Codebook Representation |
pdf |
NIQE |
NR |
SPL2012 |
Official |
Traditional |
pdf |
DIIVINE |
NR |
TIP2011 |
Official |
|
Paper Link |
Method |
Type |
Published |
Code |
Keywords |
arXiv |
AHIQ |
FR |
CVPR2022 NTIRE workshop |
Official |
Attention, Transformer |
arXiv |
JSPL |
FR |
CVPR2022 |
Official |
semi-supervised and positive-unlabeled (PU) learning |
arXiv |
CVRKD |
NAR |
AAAI2022 |
Official |
Non-Aligned content reference, knowledge distillation |
arXiv |
IQT |
FR |
CVPRW2021 |
PyTorch |
Transformer |
arXiv |
A-DISTS |
FR |
ACMM2021 |
Official |
|
arXiv |
DISTS |
FR |
TPAMI2021 |
Official |
|
arXiv |
LPIPS |
FR |
CVPR2018 |
Project |
Perceptual similarity, Pairwise Preference |
arXiv |
PieAPP |
FR |
CVPR2018 |
Project |
Perceptual similarity, Pairwise Preference |
arXiv |
WaDIQaM |
NR/FR |
TIP2018 |
Official |
|
arXiv |
JND-SalCAR |
FR |
TCSVT2020 |
|
JND (Just-Noticeable-Difference) |
|
|
|
|
|
|
pdf |
QADS |
FR |
TIP2019 |
Project |
Super-resolution |
pdf |
FSIM |
FR |
TIP2011 |
Project |
Traditional |
pdf |
VIF/IFC |
FR |
TIP2006 |
Project |
Traditional |
pdf |
MS-SSIM |
FR |
|
Project |
Traditional |
pdf |
SSIM |
FR |
TIP2004 |
Project |
Traditional |
|
PSNR |
FR |
|
|
Traditional |
Title |
Method |
Published |
Code |
Keywords |
arXiv |
NiNLoss |
ACMM2020 |
Official |
Norm-in-Norm Loss |
Paper Link |
Dataset Name |
Type |
Published |
Website |
Images |
Annotations |
arXiv |
PaQ-2-PiQ |
NR |
CVPR2020 |
Official github |
40k, 120k patches |
4M |
CVF |
SPAQ |
NR |
CVPR2020 |
Offical github |
11k (smartphone) |
|
arXiv |
KonIQ-10k |
NR |
TIP2020 |
Project |
10k from YFCC100M |
1.2M |
arXiv |
CLIVE |
NR |
TIP2016 |
Project |
1200 |
350k |
pdf |
AVA |
NR / Aesthentic |
CVPR2012 |
Github/Project |
250k (60 categories) |
|
arXiv |
PIPAL |
FR |
ECCV2020 |
Project |
250 |
1.13M |
arXiv |
KADIS-700k |
FR |
arXiv |
Project |
140k pristine / 700k distorted |
30 ratings (DCRs) per image. |
IEEE |
KADID-10k |
FR |
QoMEX2019 |
Project |
81 |
10k distortions |
pdf |
Waterloo-Exp |
FR |
TIP2017 |
Project |
4744 |
94k distortions |
pdf |
MDID |
FR |
PR2017 |
--- |
20 |
1600 distortions |
pdf |
TID2013 |
FR |
SP2015 |
Project |
25 |
3000 distortions |
pdf |
LIVEMD |
FR |
ACSSC2012 |
Project |
15 pristine images |
two successive distortions |
pdf |
CSIQ |
FR |
Journal of Electronic Imaging 2010 |
--- |
30 |
866 distortions |
pdf |
TID2008 |
FR |
2009 |
Project |
25 |
1700 distortions |
pdf |
LIVE IQA |
FR |
TIP2006 |
Project |
29 images, 780 synthetic distortions |
|
link |
IVC |
FR |
2005 |
--- |
10 |
185 distortions |
Perceptual similarity datasets
Paper Title |
Dataset Name |
Type |
Published |
Website |
Images |
Annotations |
arXiv |
BAPPS(LPIPS) |
FR |
CVPR2018 |
Project |
187.7k |
484k |
arXiv |
PieAPP |
FR |
CVPR2018 |
Project |
200 images |
2.3M |