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No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual quality of images in accordance with human subjective perception.
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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A statistical evaluation of recent full reference image quality assessment algorithms
Hamid R Sheikh, Muhammad F Sabir, and Alan C Bovik · 2006
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Modern image quality assessment
Zhou Wang and Alan C Bovik · 2006
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Most apparent distortion: full-reference image quality assessment and the role of strategy
Eric Cooper Larson and Damon Michael 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/referenceless image spatial quality evaluator
Anish Mittal, Anush K Moorthy, and Alan C Bovik · 2011
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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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No-reference image quality assessment using visual codebooks
Peng Ye and David Doermann · 2012
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Unsupervised feature learning framework for no-reference image quality assessment
Peng Ye, Jayant Kumar, Le Kang, and David Doermann · 2012
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Universal blind image quality assessment metrics via natural scene statistics and multiple kernel learning
Xinbo Gao, Fei Gao, Dacheng Tao, and Xuelong Li · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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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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Beyond human opinion scores: Blind image quality assessment based on synthetic scores
Peng Ye, Jayant Kumar, and David Doermann · 2014
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Training quality-aware filters for no-reference image quality assessment
Lin Zhang, Zhongyi Gu, Xiaoxu Liu, Hongyu Li, and Jianwei Lu · 2014
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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, et al · 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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Som: Semantic obviousness metric for image quality assessment
Peng Zhang, Wengang Zhou, Lei Wu, and Houqiang Li · 2015
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Fully deep blind image quality predictor
Jongyoo Kim and Sanghoon Lee · 2016
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Blind image quality assessment based on high order statistics aggregation
Jingtao Xu, Peng Ye, Qiaohong Li, Haiqing Du, Yong Liu, and David Doermann · 2016
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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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Perceptual quality prediction on authentically distorted images using a bag of features approach
Deepti Ghadiyaram and Alan C Bovik · 2017
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Robust video super-resolution with learned temporal dynamics
Ding Liu, Zhaowen Wang, Yuchen Fan, Xianming Liu, Zhangyang Wang, Shiyu Chang, and Thomas Huang · 2017
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Learning a no-reference quality metric for single-image super-resolution
Sgdnet: An end-to-end saliency-guided deep neural network for no-reference image quality assessment
Sheng Yang, Qiuping Jiang, Weisi Lin, and Yongtao Wang · 2019
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Assessing image quality issues for real-world problems
Tai-Yin Chiu, Yinan Zhao, and Danna Gurari · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Pipal: a large-scale image quality assessment dataset for perceptual image restoration
Gu Jinjin, Cai Haoming, Chen Haoyu, Ye Xiaoxing, Jimmy S Ren, and Dong Chao · 2020
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Thinking in frequency: Face forgery detection by mining frequency-aware clues
Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao · 2020
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Chao Ma, Chih-Yuan Yang, Xiaokang Yang, and Ming-Hsuan Yang · 2017
Cited alongside, same era.
dipiq: Blind image quality assessment by learning-to-rank discriminable image pairs
Kede Ma, Wentao Liu, Tongliang Liu, Zhou Wang, and Dacheng Tao · 2017
Cited alongside, same era.
End-to-end blind image quality assessment using deep neural networks
Kede Ma, Wentao Liu, Kai Zhang, Zhengfang Duanmu, Zhou Wang, and Wangmeng Zuo · 2017
Cited alongside, same era.
Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
On the use of deep learning for blind image quality assessment
Simone Bianco, Luigi Celona, Paolo Napoletano, and Raimondo Schettini · 2018
Cited alongside, same era.
The perception-distortion tradeoff
Yochai Blau and Tomer Michaeli · 2018
Cited alongside, same era.
Blindly assess image quality in the wild guided by a self-adaptive hyper network
Shaolin Su, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Jinqiu Sun, and Yanning Zhang · 2020
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Domain fingerprints for no-reference image quality assessment
Weihao Xia, Yujiu Yang, Jing-Hao Xue, and Jing Xiao · 2020
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From patches to pictures (paq-2-piq): Mapping the perceptual space of picture quality
Zhenqiang Ying, Haoran Niu, Praful Gupta, Dhruv Mahajan, Deepti Ghadiyaram, and Alan Bovik · 2020
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Metaiqa: Deep meta-learning for no-reference image quality assessment
Hancheng Zhu, Leida Li, Jinjian Wu, Weisheng Dong, and Guangming Shi · 2020
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Ntire 2021 challenge on perceptual image quality assessment
Jinjin Gu, Haoming Cai, Chao Dong, Jimmy S. Ren, Yu Qiao, Shuhang Gu, and Radu Timofte · 2021
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Musiq: Multi-scale image quality transformer
Junjie Ke, Qifei Wang, Yilin Wang, Peyman Milanfar, and Feng Yang · 2021
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Swinir: Image restoration using swin transformer
Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Region-adaptive deformable network for image quality assessment
Shuwei Shi, Qingyan Bai, Mingdeng Cao, Weihao Xia, Jiahao Wang, Yifan Chen, and Yujiu Yang · 2021
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Transformer for image quality assessment
Junyong You and Jari Korhonen · 2021
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Restormer: Efficient transformer for high-resolution image restoration
Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang · 2021
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Learning conditional knowledge distillation for degraded-reference image quality assessment
Heliang Zheng, Huan Yang, Jianlong Fu, Zheng-Jun Zha, and Jiebo Luo · 2021
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Vdtr: Video deblurring with transformer
Mingdeng Cao, Yanbo Fan, Yong Zhang, Jue Wang, and Yujiu Yang · 2022
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No-reference image quality assessment via transformers, relative ranking, and self-consistency
S Alireza Golestaneh, Saba Dadsetan, and Kris M Kitani · 2022
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NTIRE 2022 challenge on perceptual image quality assessment
Jinjin Gu, Haoming Cai, Chao Dong, Jimmy S. Ren, Radu Timofte, et al · 2022
Closest in time.