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Image quality assessment (IQA) focuses on the perceptual visual quality of images, playing a crucial role in downstream tasks such as image reconstruction, compression, and generation.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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Live image quality assessment database release 2
H Sheikh · 2005
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Image information and visual quality
Hamid R Sheikh and Alan C Bovik · 2006
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Most apparent distortion: full-reference image quality assessment and the role of strategy
Eric C Larson and Damon M Chandler · 2010
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A two-step framework for constructing blind image quality indices
Anush Krishna Moorthy and Alan Conrad Bovik · 2010
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Blind image quality assessment: From natural scene statistics to perceptual quality
Anush Krishna Moorthy and Alan Conrad Bovik · 2011
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Fsim: A feature similarity index for image quality assessment
Lin Zhang, Lei Zhang, Xuanqin Mou, and David Zhang · 2011
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No-reference image quality assessment in the spatial domain
Anish Mittal, Anush Krishna Moorthy, and Alan Conrad Bovik · 2012
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Making a “completely blind” image quality analyzer
Anish Mittal, Rajiv Soundararajan, and Alan C Bovik · 2012
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Blind image quality assessment: A natural scene statistics approach in the dct domain
Michele A Saad, Alan C Bovik, and Christophe Charrier · 2012
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Convolutional neural networks for no-reference image quality assessment
Le Kang, Peng Ye, Yi Li, and David Doermann · 2014
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Live in the wild image quality challenge database
Deepti Ghadiyaram and Alan C Bovik · 2015
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Image database tid2013: Peculiarities, results and perspectives
Nikolay Ponomarenko, Lina Jin, Oleg Ieremeiev, Vladimir Lukin, Karen Egiazarian, Jaakko Astola, Benoit Vozel, Kacem Chehdi, Marco Carli, Federica Battisti, and C.-C. Jay Kuo · 2015
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A feature-enriched completely blind image quality evaluator
Lin Zhang, Lei Zhang, and Alan C Bovik · 2015
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Deep neural networks for no-reference and full-reference image quality assessment
Sebastian Bosse, Dominique Maniry, Klaus-Robert Müller, Thomas Wiegand, and Wojciech Samek · 2017
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Rankiqa: Learning from rankings for no-reference image quality assessment
Xialei Liu, Joost Van De Weijer, and Andrew D Bagdanov · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Learning a no-reference quality metric for single-image super-resolution
Chao Ma, Chih-Yuan Yang, Xiaokang Yang, and Ming-Hsuan Yang · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Blind predicting similar quality map for image quality assessment
Da Pan, Ping Shi, Ming Hou, Zefeng Ying, Sizhe Fu, and Yuan Zhang · 2018
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Pieapp: Perceptual image-error assessment through pairwise preference
Ekta Prashnani, Hong Cai, Yasamin Mostofi, and Pradeep Sen · 2018
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Nima: Neural image assessment
Hossein Talebi and Peyman Milanfar · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Kadid-10k: A large-scale artificially distorted iqa database
Hanhe Lin, Vlad Hosu, and Dietmar Saupe · 2019
Cited alongside, same era.
Image quality assessment: Unifying structure and texture similarity
Keyan Ding, Kede Ma, Shiqi Wang, and Eero P Simoncelli · 2020
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Perceptual quality assessment of smartphone photography
Yuming Fang, Hanwei Zhu, Yan Zeng, Kede Ma, and Zhou Wang · 2020
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Pipal: a large-scale image quality assessment dataset for perceptual image restoration
Jinjin GU, Haoming Cai, Haoyu Chen, Xiaoxing Ye, Ren Jimmy S, and Chao Dong · 2020
Cited alongside, same era.
Koniq-10k: An ecologically valid database for deep learning of blind image quality assessment
Vlad Hosu, Hanhe Lin, Tamas Sziranyi, and Dietmar Saupe · 2020
Cited alongside, same era.
Blindly assess image quality in the wild guided by a self-adaptive hyper network
Qwen2.5-coder technical report
Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Keming Lu, et al · 2024
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al · 2024
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Q-instruct: Improving low-level visual abilities for multi-modality foundation models
Haoning Wu, Zicheng Zhang, Erli Zhang, Chaofeng Chen, Liang Liao, Annan Wang, Kaixin Xu, Chunyi Li, Jingwen Hou, Guangtao Zhai, et al · 2024
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Q-align: Teaching LMMs for visual scoring via discrete text-defined levels
Haoning Wu, Zicheng Zhang, Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, et al · 2024
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Towards open-ended visual quality comparison
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Shaolin Su, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Jinqiu Sun, and Yanning Zhang · 2020
Cited alongside, same era.
Metaiqa: Deep meta-learning for no-reference image quality assessment
Hancheng Zhu, Leida Li, Jinjian Wu, Weisheng Dong, and Guangming Shi · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Locally adaptive structure and texture similarity for image quality assessment
Keyan Ding, Yi Liu, Xueyi Zou, Shiqi Wang, and Kede Ma · 2021
Cited alongside, same era.
Musiq: Multi-scale image quality transformer
Junjie Ke, Qifei Wang, Yilin Wang, Peyman Milanfar, and Feng Yang · 2021
Cited alongside, same era.
Learning conditional knowledge distillation for degraded-reference image quality assessment
Heliang Zheng, Huan Yang, Jianlong Fu, Zheng-Jun Zha, and Jiebo Luo · 2021
Cited alongside, same era.
Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al · 2022
Cited alongside, same era.
Haoning Wu, Hanwei Zhu, Zicheng Zhang, Erli Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Annan Wang, Wenxiu Sun, Qiong Yan, et al · 2024
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Qwen2.5-math technical report: Toward mathematical expert model via self-improvement
An Yang, Beichen Zhang, Binyuan Hui, Bofei Gao, Bowen Yu, Chengpeng Li, Dayiheng Liu, Jianhong Tu, Jingren Zhou, Junyang Lin, et al · 2024
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Internlm-math: Open math large language models toward verifiable reasoning
Huaiyuan Ying, Shuo Zhang, Linyang Li, Zhejian Zhou, Yunfan Shao, Zhaoye Fei, Yichuan Ma, Jiawei Hong, Kuikun Liu, Ziyi Wang, et al · 2024
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Descriptive image quality assessment in the wild
Zhiyuan You, Jinjin Gu, Zheyuan Li, Xin Cai, Kaiwen Zhu, Chao Dong, and Tianfan Xue · 2024
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Depicting beyond scores: Advancing image quality assessment through multi-modal language models
Zhiyuan You, Zheyuan Li, Jinjin Gu, Zhenfei Yin, Tianfan Xue, and Chao Dong · 2024
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Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback
Tianyu Yu, Yuan Yao, Haoye Zhang, Taiwen He, Yifeng Han, Ganqu Cui, Jinyi Hu, Zhiyuan Liu, Hai-Tao Zheng, Maosong Sun, et al · 2024
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Rlaif-v: Aligning mllms through open-source ai feedback for super gpt-4v trustworthiness
Tianyu Yu, Haoye Zhang, Yuan Yao, Yunkai Dang, Da Chen, Xiaoman Lu, Ganqu Cui, Taiwen He, Zhiyuan Liu, Tat-Seng Chua, et al · 2024
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o1-coder: an o1 replication for coding
Yuxiang Zhang, Shangxi Wu, Yuqi Yang, Jiangming Shu, Jinlin Xiao, Chao Kong, and Jitao Sang · 2024
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Adaptive image quality assessment via teaching large multimodal model to compare
Hanwei Zhu, Haoning Wu, Yixuan Li, Zicheng Zhang, Baoliang Chen, Lingyu Zhu, Yuming Fang, Guangtao Zhai, Weisi Lin, and Shiqi Wang · 2024
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Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al · 2025
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Toward generalized image quality assessment: Relaxing the perfect reference quality assumption
Du Chen, Tianhe Wu, Kede Ma, and Lei Zhang · 2025
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
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Visual-rft: Visual reinforcement fine-tuning
Ziyu Liu, Zeyi Sun, Yuhang Zang, Xiaoyi Dong, Yuhang Cao, Haodong Duan, Dahua Lin, and Jiaqi Wang · 2025
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Jiazhen Pan, Che Liu, Junde Wu, Fenglin Liu, Jiayuan Zhu, Hongwei Bran Li, Chen Chen, Cheng Ouyang, and Daniel Rueckert · 2025
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Q-bench: A benchmark for general-purpose foundation models on low-level vision
Haoning Wu, Zicheng Zhang, Erli Zhang, Chaofeng Chen, Liang Liao, Annan Wang, Chunyi Li, Wenxiu Sun, Qiong Yan, Guangtao Zhai, et al · 2025
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Teaching large language models to regress accurate image quality scores using score distribution
Zhiyuan You, Xin Cai, Jinjin Gu, Tianfan Xue, and Chao Dong · 2025
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Jingyi Zhang, Jiaxing Huang, Huanjin Yao, Shunyu Liu, Xikun Zhang, Shijian Lu, and Dacheng Tao · 2025
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Q-bench-video: Benchmarking the video quality understanding of lmms
Zicheng Zhang, Ziheng Jia, Haoning Wu, Chunyi Li, Zijian Chen, Yingjie Zhou, Wei Sun, Xiaohong Liu, Xiongkuo Min, Weisi Lin, et al · 2025
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Q-eval-100k: Evaluating visual quality and alignment level for text-to-vision content
Zicheng Zhang, Tengchuan Kou, Shushi Wang, Chunyi Li, Wei Sun, Wei Wang, Xiaoyu Li, Zongyu Wang, Xuezhi Cao, Xiongkuo Min, et al · 2025
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Teaching lmms for image quality scoring and interpreting
Zicheng Zhang, Haoning Wu, Ziheng Jia, Weisi Lin, and Guangtao Zhai · 2025
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An intelligent agentic system for complex image restoration problems
Kaiwen Zhu, Jinjin Gu, Zhiyuan You, Yu Qiao, and Chao Dong · 2025
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