Fetching the paper…
Reading the bibliography…
Generally, humans are more skilled at perceiving differences between high-quality (HQ) and low-quality (LQ) images than directly judging the quality of a single LQ image.
Digital image restoration
Banham, M. R.; and Katsaggelos, A. K. 1997 · 1997
Earlier work this paper cites.
Final report from the video quality experts group on the validation of objective models of video quality assessment march 2000
Antkowiak, J.; Jamal Baina, T.; Baroncini, F. V.; Chateau, N.; FranceTelecom, F.; Pessoa, A. C. F.; Stephanie Colonnese, F.; Contin, I. L.; Caviedes, J.; and Philips, F. 2000 · 2000
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Wang, Z.; Bovik, A. C.; Sheikh, H. R.; and Simoncelli, E. P. 2004 · 2004
Earlier work this paper cites.
Image information and visual quality
Sheikh, H. R.; and Bovik, A. C. 2006 · 2006
Earlier work this paper cites.
A statistical evaluation of recent full reference image quality assessment algorithms
Sheikh, H. R.; Sabir, M. F.; and Bovik, A. C. 2006 · 2006
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
Earlier work this paper cites.
TID2008-a database for evaluation of full-reference visual quality assessment metrics
Ponomarenko, N.; Lukin, V.; Zelensky, A.; Egiazarian, K.; Carli, M.; and Battisti, F. 2009 · 2009
Earlier work this paper cites.
Most apparent distortion: full-reference image quality assessment and the role of strategy
Larson, E. C.; and Chandler, D. M. 2010 · 2010
Earlier work this paper cites.
FSIM: A feature similarity index for image quality assessment
Zhang, L.; Zhang, L.; Mou, X.; and Zhang, D. 2011 · 2011
Earlier work this paper cites.
Reduced-reference image quality assessment by structural similarity estimation
Rehman, A.; and Wang, Z. 2012 · 2012
Earlier work this paper cites.
Do Deep Nets Really Need to be Deep?
Ba, J.; and Caruana, R. 2014 · 2014
Earlier work this paper cites.
Convolutional neural networks for no-reference image quality assessment
Kang, L.; Ye, P.; Li, Y.; and Doermann, D. 2014 · 2014
Earlier work this paper cites.
Image super-resolution using deep convolutional networks
Dong, C.; Loy, C. C.; He, K.; and Tang, X. 2015 · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
Cited alongside, same era.
Image database TID2013: Peculiarities, results and perspectives
Ponomarenko, N.; Jin, L.; Ieremeiev, O.; Lukin, V.; Egiazarian, K.; Astola, J.; Vozel, B.; Chehdi, K.; Carli, M.; Battisti, F.; et al. 2015 · 2015
Cited alongside, same era.
Cross modal distillation for supervision transfer
Gupta, S.; Hoffman, J.; and Malik, J. 2016 · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Cited alongside, same era.
Image quality assessment using similar scene as reference
Liang, Y.; Wang, J.; Wan, X.; Gong, Y.; and Zheng, N. 2016 · 2016
Cited alongside, same era.
Blind image quality assessment based on high order statistics aggregation
Pieapp: Perceptual image-error assessment through pairwise preference
Prashnani, E.; Cai, H.; Mostofi, Y.; and Sen, P. 2018 · 2018
Later among the works it cites.
The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R.; Isola, P.; Efros, A. A.; Shechtman, E.; and Wang, O. 2018 · 2018
Later among the works it cites.
KADID-10k: A large-scale artificially distorted IQA database
Lin, H.; Hosu, V.; and Saupe, D. 2019 · 2019
Later among the works it cites.
KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment
Hosu, V.; Lin, H.; Sziranyi, T.; and Saupe, D. 2020 · 2020
Later among the works it cites.
Norm-in-norm loss with faster convergence and better performance for image quality assessment
Li, D.; Jiang, T.; and Jiang, M. 2020 · 2020
Later among the works it cites.
Robust re-identification by multiple views knowledge distillation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Xu, J.; Ye, P.; Li, Q.; Du, H.; Liu, Y.; and Doermann, D. 2016 · 2016
Cited alongside, same era.
Ntire 2017 challenge on single image super-resolution: Dataset and study
Agustsson, E.; and Timofte, R. 2017 · 2017
Cited alongside, same era.
Deep neural networks for no-reference and full-reference image quality assessment
Bosse, S.; Maniry, D.; Müller, K.-R.; Wiegand, T.; and Samek, W. 2017 · 2017
Cited alongside, same era.
dipIQ: Blind image quality assessment by learning-to-rank discriminable image pairs
Ma, K.; Liu, W.; Liu, T.; Wang, Z.; and Tao, D. 2017 · 2017
Cited alongside, same era.
Ntire 2017 challenge on single image super-resolution: Methods and results
Timofte, R.; Agustsson, E.; Van Gool, L.; Yang, M.-H.; and Zhang, L. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
Modality distillation with multiple stream networks for action recognition
Garcia, N. C.; Morerio, P.; and Murino, V. 2018 · 2018
Cited alongside, same era.
Porrello, A.; Bergamini, L.; and Calderara, S. 2020 · 2020
Later among the works it cites.
Blindly assess image quality in the wild guided by a self-adaptive hyper network
Su, S.; Yan, Q.; Zhu, Y.; Zhang, C.; Ge, X.; Sun, J.; and Zhang, Y. 2020 · 2020
Later among the works it cites.
End-to-end blind image quality prediction with cascaded deep neural network
Wu, J.; Ma, J.; Liang, F.; Dong, W.; Shi, G.; and Lin, W. 2020 · 2020
Later among the works it cites.
Perceptual image quality assessment with transformers
Cheon, M.; Yoon, S.-J.; Kang, B.; and Lee, J. 2021 · 2021
Later among the works it cites.
Comparison of full-reference image quality models for optimization of image processing systems
Ding, K.; Ma, K.; Wang, S.; and Simoncelli, E. P. 2021 · 2021
Later among the works it cites.
Mlp-mixer: An all-mlp architecture for vision
Tolstikhin, I.; Houlsby, N.; Kolesnikov, A.; Beyer, L.; Zhai, X.; Unterthiner, T.; Yung, J.; Steiner, A. P.; Keysers, D.; Uszkoreit, J.; et al. 2021 · 2021
Later among the works it cites.
Transformer for image quality assessment
You, J.; and Korhonen, J. 2021 · 2021
Later among the works it cites.