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Awesome-Image-Quality-Assessment
A comprehensive collection of IQA papers
https://github.com/chaofengc/Awesome-Image-Quality-Assessment
Last synced: 1 day ago
JSON representation
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Papers
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Image Aesthetic Assessment
- ECCV2024 - L993) | [Project](https://yeolj00.github.io/personal-projects/personalized-aesthetics/)
- Arxiv 2024 - L904)
- CVPR2023 - L789)
- CVPR2023 - T/BAID) | [Bibtex](./iqa_ref.bib#L777-L782)
- ACMMM 2024 - expert/) | [Github](https://github.com/yipoh/AesExpert) | [Bibtex](./iqa_ref.bib#L1037-L1042)
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No Reference (NR)
- Arxiv 2024 - L986)
- CVPR 2024 - L961)
- arXiv - wise quality map
- arXiv - PyTorch) | Two branches for synthetic and authentic distortions
- Arxiv 2024 - L954)
- - L920)
- WACV2024 - L865)
- TIP2023 - PyTorch) | [Bibtex](./iqa_ref.bib#L892-L897)
- ICCV2023 - IQA) | [Bibtex](./iqa_ref.bib#L798-L804)
- CVPR2023 - L796) | [Github](https://github.com/avinabsaha/ReIQA)
- CVPR2023 - L768)
- AAAI2023 - IQA) | [Bibtex](./iqa_ref.bib#L745-L750)
- AAAI2023 - L750)
- - L743)
- TIP2022 - L856)
- TIP2022 - Videocoding/VCRNet) | [Bibtex](./iqa_ref.bib#L752-L761)
- TMM2022 - L707)
- CVPR2021 - L722)
- arXiv - dimension attention, dual branch |
- arXiv - consistency |
- pdf - task with distortion prediction
- arXiv - research/google-research/tree/master/musiq) / [Pytorch](https://github.com/anse3832/MUSIQ) | Multi-scale, transformer, Aspect Ratio Preserved (ARP) resizing
- arXiv
- pdf - aware hyper network
- arXiv - IQA | NR | CVPR2020 | [Official](https://github.com/zhuhancheng/MetaIQA) | Meta-learning
- arXiv
- arXiv - NRQM)).
- arXiv
- arXiv - wise quality map
- arXiv
- pdf - based NR-IQA
- arXiv
- arXiv - PyTorch) | Two branches for synthetic and authentic distortions
- pdf - IMage-Assessment)/[Tensorflow](https://github.com/idealo/image-quality-assessment) | Squared EMD loss
- pdf - task: distortion learning and quality prediction
- arXiv
- arXiv - metric) | Traditional, Super resolution
- arXiv
- IEEE
- pdf - II | NR | TIP2012 | [Official](https://github.com/utlive/BLIINDS2) |
- Arxiv 2024 - L971)
- CVPR 2024 - mcl/QCN?tab=readme-ov-file) | [Bibtex](./iqa_ref.bib#L1074-L1079)
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Others
- CVPR2024 - L978) | [Project](https://dsl-fiqa.github.io/)
- - L1000) | [Github](https://github.com/TianheWu/Assessor360)
- - L1016) | [Github](https://github.com/IntMeGroup/ESIQA)
- Arxiv 2024 - L1035) | [Github](https://github.com/msu-video-group/adversarial-defenses-for-iqa) | [Project](https://videoprocessing.ai/benchmarks/iqa-defenses.html)
- NeurIPS 2022 - L1025) | [Github](https://github.com/zwx8981/PerceptualAttack_BIQA)
- arXiv - in-Norm Loss
- ECCV 2024 - hjq/MS-SWD) | [Bibtex](./iqa_ref.bib#L1051-L1058)
- CVPR 2023 - Flow) | [Bibtex](./iqa_ref.bib#L1060-L1065)
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Explainable IQA
- ACM MM2024 - L1007) | [Github](https://github.com/Q-Future/Q-Ground)
- Arxiv 2024 - L947)
- ECCV 2024 - L929)
- ECCV 2024 - Future/Co-Instruct) | [Bibtex](./iqa_ref.bib#L906-L913)
- ECCV 2024 - L890)
- CVPR 2024 - Future/Q-Instruct) | [Bibtex](./iqa_ref.bib#L876-L883)
- TPAMI 2024 - Future/Q-Bench) | [Bibtex](./iqa_ref.bib#L1044-L1049)
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Unified IQA
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AIGC IQA
- ICLR 2024 - Future/Q-Bench) | [Bibtex](./iqa_ref.bib#L833-L838)
- ICCV 2023 - Hu/tifa) | [Bibtex](./iqa_ref.bib#L840-L846) | [Project](https://tifa-benchmark.github.io/)
- NeurIPS 2023 - L822)
- ICCV2023 - L809)
- NeurIPS 2023 - L815)
- TCSVT2023 - 3k-Database) | [Bibtex](./iqa_ref.bib#L824-L831)
- CVPR 2024 (best paper) - research-datasets/richhf-18k) | [Bibtex](./iqa_ref.bib#L1067-L1072)
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Full Reference (FR)
- arXiv
- ECCV2022 - LPIPS) | [Bibtex](./iqa_ref.bib#L731-L736)
- BMVC2022 - L729)
- ACM MM2022 - geek/SRIF) | [Bibtex](./iqa_ref.bib#L709-L714)
- arXiv
- arXiv - supervised and positive-unlabeled (PU) learning |
- arXiv - IQA) | Non-Aligned content reference, knowledge distillation
- arXiv
- arXiv - DISTS | FR | ACMM2021 | [Official](https://github.com/dingkeyan93/A-DISTS) |
- arXiv
- arXiv - SalCAR | FR| TCSVT2020 | []() | JND (Just-Noticeable-Difference)
- pdf - resolution/) | Super-resolution
- pdf - SSIM | FR | | [Project](https://ece.uwaterloo.ca/~z70wang/research/ssim/) | Traditional
- arXiv
- arXiv
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ROI-based IQA
- Arxiv 2024 - L1089)
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Uncategorized
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Uncategorized
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Datasets
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IQA datasets
- arXiv - 2-PiQ | NR | CVPR2020 | [Official github](https://github.com/baidut/PaQ-2-PiQ) | 40k, 120k patches | 4M |
- CVF
- arXiv - 10k | NR | TIP2020 | [Project](http://database.mmsp-kn.de/koniq-10k-database.html) | 10k from [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/) | 1.2M |
- arXiv
- arXiv
- arXiv
- arXiv - 700k | FR | arXiv | [Project](http://database.mmsp-kn.de/kadid-10k-database.html) | 140k pristine / 700k distorted | 30 ratings (DCRs) per image. |
- IEEE - 10k | FR | QoMEX2019 | [Project](http://database.mmsp-kn.de/kadid-10k-database.html) | 81 | 10k distortions |
- pdf - Exp | FR | TIP2017 | [Project](https://ece.uwaterloo.ca/~k29ma/exploration/) | 4744 | 94k distortions |
- pdf - -- | 20 | 1600 distortions |
- pdf - -- | 30 | 866 distortions |
- link - -- | 10 | 185 distortions |
- arXiv - IQA | NR | ECCVW2024 | [Project](https://database.mmsp-kn.de/uhd-iqa-benchmark-database.html) | 6k (~3840x2160) | 20 ratings per image |
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