Fetching the paper…
Reading the bibliography…
Artificial Intelligence Generated Content (AIGC) has grown rapidly in recent years, among which AI-based image generation has gained widespread attention due to its efficient and imaginative image creation ability.
H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms,” IEEE Transactions on Image Processing (TIP) , pp. 3440–3451, 2006
2006
Earlier work this paper cites.
N. D. Narvekar and L. J. Karam, “A no-reference perceptual image sharpness metric based on a cumulative probability of blur detection,” in International Workshop on Quality of Multimedia Experience (QoMEX) , 2009, pp. 87–91
2009
Earlier work this paper cites.
E. C. Larson and D. M. Chandler, “Most apparent distortion: full-reference image quality assessment and the role of strategy,” Journal of Electronic Imaging (JEI) , vol. 19, no. 1, 2010
2010
Earlier work this paper cites.
A. K. Moorthy and A. C. Bovik, “Blind image quality assessment: From natural scene statistics to perceptual quality,” IEEE Transactions on Image Processing (TIP) , pp. 3350–3364, 2011
2011
Earlier work this paper cites.
N. Murray, L. Marchesotti, and F. Perronnin, “AVA: A large-scale database for aesthetic visual analysis,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 2408–2415, 2012
2012
Earlier work this paper cites.
A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a “completely blind” image quality analyzer,” IEEE Signal Processing Letters (SPL) , pp. 209–212, 2012
2012
Earlier work this paper cites.
A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Transactions on Image Processing (TIP) , pp. 4695–4708, 2012
2012
Earlier work this paper cites.
M. A. Saad, A. C. Bovik, and C. Charrier, “Blind image quality assessment: A natural scene statistics approach in the dct domain,” IEEE Transactions on Image Processing (TIP) , vol. 21, no. 8, 2012
2012
Earlier work this paper cites.
P. Ye, J. Kumar, L. Kang, and D. Doermann, “Unsupervised feature learning framework for no-reference image quality assessment,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2012, pp. 1098–1105
2012
Earlier work this paper cites.
W. Xue, L. Zhang, and X. Mou, “Learning without human scores for blind image quality assessment,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2013, pp. 995–1002
2013
Earlier work this paper cites.
L. Kang, P. Ye, Y. Li, and D. Doermann, “Convolutional neural networks for no-reference image quality assessment,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2014, pp. 1733–1740
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
K. Gu, G. Zhai, X. Yang, and W. Zhang, “Hybrid no-reference quality metric for singly and multiply distorted images,” IEEE Transactions on Broadcasting (TBC) , pp. 555–567, 2014
2014
Earlier work this paper cites.
W. Xue, X. Mou, L. Zhang, A. C. Bovik, and X. Feng, “Blind image quality assessment using joint statistics of gradient magnitude and laplacian features,” IEEE Transactions on Image Processing (TIP) , pp. 4850–4862, 2014
2014
Earlier work this paper cites.
N. Ponomarenko, L. Jin, O. Ieremeiev, V. Lukin, K. Egiazarian, J. Astola, B. Vozel, K. Chehdi, M. Carli, F. Battisti et al. , “Image database tid2013: Peculiarities, results and perspectives,” Signal Processing: Image Communication (SPIC) , vol. 30, 2015
2015
Earlier work this paper cites.
D. Ghadiyaram and A. C. Bovik, “Massive online crowdsourced study of subjective and objective picture quality,” IEEE Transactions on Image Processing (TIP) , vol. 25, 2015
2015
Earlier work this paper cites.
L. Zhang, L. Zhang, and A. C. Bovik, “A feature-enriched completely blind image quality evaluator,” IEEE Transactions on Image Processing (TIP) , pp. 2579–2591, 2015
2015
Earlier work this paper cites.
K. Gu, S. Wang, H. Yang, W. Lin, G. Zhai, X. Yang, and W. Zhang, “Saliency-guided quality assessment of screen content images,” IEEE Transactions on Multimedia (TMM) , vol. 18, no. 6, pp. 1098–1110, 2016
2016
Earlier work this paper cites.
K. Gu, S. Wang, G. Zhai, S. Ma, X. Yang, W. Lin, W. Zhang, and W. Gao, “Blind quality assessment of tone-mapped images via analysis of information, naturalness, and structure,” IEEE Transactions on Multimedia (TMM) , vol. 18, no. 3, pp. 432–443, 2016
2016
Earlier work this paper cites.
S. Kong, X. Shen, Z. Lin, R. Mech, and C. Fowlkes, “Photo aesthetics ranking network with attributes and content adaptation,” in European Conference on Computer Vision (ECCV) , 2016, pp. 662–679
2016
Earlier work this paper cites.
J. Xu, P. Ye, Q. Li, H. Du, Y. Liu, and D. Doermann, “Blind image quality assessment based on high order statistics aggregation,” IEEE Transactions on Image Processing (TIP) , pp. 4444–4457, 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
X. Min, K. Ma, K. Gu, G. Zhai, Z. Wang, and W. Lin, “Unified blind quality assessment of compressed natural, graphic, and screen content images,” IEEE Transactions on Image Processing (TIP) , vol. 26, no. 11, pp. 5462–5474, 2017
2017
Earlier work this paper cites.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,” Proceedings of the Advances in Neural Information Processing Systems (NeurIPS) , 2017
2017
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Proceedings of the Advances in Neural Information Processing Systems (NeurIPS) , 2017
2017
Earlier work this paper cites.
X. Min, K. Gu, G. Zhai, J. Liu, X. Yang, and C. W. Chen, “Blind quality assessment based on pseudo-reference image,” IEEE Transactions on Multimedia (TMM) , pp. 2049–2062, 2017
2017
Earlier work this paper cites.
D. Kundu, D. Ghadiyaram, A. C. Bovik, and B. L. Evans, “Large-scale crowdsourced study for tone-mapped hdr pictures,” IEEE Transactions on Image Processing (TIP) , pp. 4725–4740, 2017
2017
Earlier work this paper cites.
S. Bosse, D. Maniry, K.-R. Müller, T. Wiegand, and W. Samek, “Deep neural networks for no-reference and full-reference image quality assessment,” IEEE Transactions on Image Processing (TIP) , pp. 206–219, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. Kim and S. Lee, “Fully deep blind image quality predictor,” IEEE Journal of Selected Topics in Signal Processing (J-STSP) , vol. 11, no. 1, pp. 206–220, 2017
2017
Earlier work this paper cites.
K. Ma, W. Liu, K. Zhang, Z. Duanmu, Z. Wang, and W. Zuo, “End-to-end blind image quality assessment using deep neural networks,” IEEE Transactions on Image Processing (TIP) , vol. 27, no. 3, pp. 1202–1213, 2017
2017
Earlier work this paper cites.
D. Ghadiyaram and A. C. Bovik, “Perceptual quality prediction on authentically distorted images using a bag of features approach,” Journal of Vision (JOV) , pp. 32–32, 2017
2017
Earlier work this paper cites.
X. Min, G. Zhai, K. Gu, X. Yang, and X. Guan, “Objective quality evaluation of dehazed images,” IEEE Transactions on Intelligent Transportation Systems (TITS) , vol. 20, no. 8, pp. 2879–2892, 2018
2018
Earlier work this paper cites.
X. Min, G. Zhai, K. Gu, Y. Liu, and X. Yang, “Blind image quality estimation via distortion aggravation,” IEEE Transactions on Broadcasting (TBC) , vol. 64, no. 2, pp. 508–517, 2018
2018
Earlier work this paper cites.
X. Min, G. Zhai, K. Gu, Y. Liu, and X. Yang, “Blind image quality estimation via distortion aggravation,” IEEE Transactions on Broadcasting (TBC) , pp. 508–517, 2018
2018
Earlier work this paper cites.
W. Zhang, K. Ma, J. Yan, D. Deng, and Z. Wang, “Blind image quality assessment using a deep bilinear convolutional neural network,” IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) , pp. 36–47, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
H. Talebi and P. Milanfar, “Nima: Neural image assessment,” IEEE Transactions on Image Processing (TIP) , pp. 3998–4011, 2018
2018
Earlier work this paper cites.
X. Min, G. Zhai, K. Gu, Y. Zhu, J. Zhou, G. Guo, X. Yang, X. Guan, and W. Zhang, “Quality evaluation of image dehazing methods using synthetic hazy images,” IEEE Transactions on Multimedia (TMM) , vol. 21, no. 9, pp. 2319–2333, 2019
2019
Cited alongside, same era.
H. Lin, V. Hosu, and D. Saupe, “Kadid-10k: A large-scale artificially distorted iqa database,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX) . IEEE, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
H. Zeng, Z. Cao, L. Zhang, and A. C. Bovik, “A unified probabilistic formulation of image aesthetic assessment,” IEEE Transactions on Image Processing (TIP) , pp. 1548–1561, 2019
2019
Cited alongside, same era.
S. A. Golestaneh, S. Dadsetan, and K. M. Kitani, “No-reference image quality assessment via transformers, relative ranking, and self-consistency,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , 2022, pp. 1220–1230
2022
Later among the works it cites.
P. C. Madhusudana, N. Birkbeck, Y. Wang, B. Adsumilli, and A. C. Bovik, “Image quality assessment using contrastive learning,” IEEE Transactions on Image Processing (TIP) , vol. 31, pp. 4149–4161, 2022
2022
Later among the works it cites.
F. Bao, S. Nie, K. Xue, C. Li, S. Pu, Y. Wang, G. Yue, Y. Cao, H. Su, and J. Zhu, “One transformer fits all distributions in multi-modal diffusion at scale,” in International Conference on Machine Learning (ICML) , 2023, pp. 1692–1717
2023
Later among the works it cites.
Y. Fu, H. Liu, Y. Zou, S. Wang, Z. Li, and D. Zheng, “Category-level band learning based feature extraction for hyperspectral image classification,” IEEE Transactions on Geoscience and Remote Sensing (TGRS) , vol. 62, pp. 1–16, 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
V. Hosu, B. Goldlucke, and D. Saupe, “Effective aesthetics prediction with multi-level spatially pooled features,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 9375–9383
2019
Cited alongside, same era.
K. Ding, K. Ma, S. Wang, and E. P. Simoncelli, “Image quality assessment: Unifying structure and texture similarity,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , vol. 44, no. 5, pp. 2567–2581, 2020
2020
Cited alongside, same era.
V. Hosu, H. Lin, T. Sziranyi, and D. Saupe, “Koniq-10k: An ecologically valid database for deep learning of blind image quality assessment,” IEEE Transactions on Image Processing (TIP) , pp. 4041–4056, 2020
2020
Cited alongside, same era.
Y. Fang, H. Zhu, Y. Zeng, K. Ma, and Z. Wang, “Perceptual quality assessment of smartphone photography,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 3677–3686
2020
Cited alongside, same era.
G. Zhai and X. Min, “Perceptual image quality assessment: a survey,” Science China Information Sciences (SCIS) , vol. 63, pp. 1–52, 2020
2020
Cited alongside, same era.
X. Min, G. Zhai, J. Zhou, M. C. Farias, and A. C. Bovik, “Study of subjective and objective quality assessment of audio-visual signals,” IEEE Transactions on Image Processing (TIP) , vol. 29, pp. 6054–6068, 2020
2020
Cited alongside, same era.
S. Su, Q. Yan, Y. Zhu, C. Zhang, X. Ge, J. Sun, and Y. Zhang, “Blindly assess image quality in the wild guided by a self-adaptive hyper network,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 3667–3676
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2023
Later among the works it cites.
L. Chen, Y. Fu, K. Wei, D. Zheng, and F. Heide, “Instance segmentation in the dark,” International Journal of Computer Vision (IJCV) , vol. 131, no. 8, pp. 2198–2218, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Wang, H. Duan, J. Liu, S. Chen, X. Min, and G. Zhai, “Aigciqa2023: A large-scale image quality assessment database for ai generated images: from the perspectives of quality, authenticity and correspondence,” in CAAI International Conference on Artificial Intelligence (CICAI) , 2023, pp. 46–57
2023
Later among the works it cites.
H. Duan, X. Min, W. Sun, Y. Zhu, X.-P. Zhang, and G. Zhai, “Attentive deep image quality assessment for omnidirectional stitching,” IEEE Journal of Selected Topics in Signal Processing (J-STSP) , 2023
2023
Later among the works it cites.
T. Guan, C. Li, Y. Zheng, X. Wu, and A. C. Bovik, “Dual-stream complex-valued convolutional network for authentic dehazed image quality assessment,” IEEE Transactions on Image Processing (TIP) , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
W. Sun, X. Min, D. Tu, S. Ma, and G. Zhai, “Blind quality assessment for in-the-wild images via hierarchical feature fusion and iterative mixed database training,” IEEE Journal of Selected Topics in Signal Processing (J-STSP) , 2023
2023
Later among the works it cites.
Y. Li, H. Fan, R. Hu, C. Feichtenhofer, and K. He, “Scaling language-image pre-training via masking,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 23 390–23 400
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Fang, W. Wang, B. Xie, Q. Sun, L. Wu, X. Wang, T. Huang, X. Wang, and Y. Cao, “Eva: Exploring the limits of masked visual representation learning at scale,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 19 358–19 369
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Wang, K. C. Chan, and C. C. Loy, “Exploring clip for assessing the look and feel of images,” in AAAI , 2023
2023
Later among the works it cites.
A. Saha, S. Mishra, and A. C. Bovik, “Re-iqa: Unsupervised learning for image quality assessment in the wild,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 5846–5855
2023
Later among the works it cites.
W. Zhang, G. Zhai, Y. Wei, X. Yang, and K. Ma, “Blind image quality assessment via vision-language correspondence: A multitask learning perspective,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 14 071–14 081
2023
Later among the works it cites.
H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” Proceedings of the Advances in Neural Information Processing Systems (NeurIPS) , 2023
2023
Later among the works it cites.
2024
Closest in time.
L. Chen, Y. Fu, L. Gu, C. Yan, T. Harada, and G. Huang, “Frequency-aware feature fusion for dense image prediction,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , vol. 46, no. 12, pp. 10 763–10 780, 2024
2024
Closest in time.
Z. Lai, Y. Fu, and J. Zhang, “Hyperspectral image super resolution with real unaligned rgb guidance,” IEEE Transactions on Neural Networks and Learning Systems (TNNLS) , vol. 1, no. 1, pp. 1–13, 2024
2024
Closest in time.
2024
Closest in time.
T. Zhou, S. Tan, W. Zhou, Y. Luo, Y.-G. Wang, and G. Yue, “Adaptive mixed-scale feature fusion network for blind ai-generated image quality assessment,” IEEE Transactions on Broadcasting (TBC) , 2024
2024
Closest in time.
H. Duan, X. Zhu, Y. Zhu, X. Min, and G. Zhai, “A quick review of human perception in immersive media,” IEEE Open Journal on Immersive Displays (OJID) , 2024
2024
Closest in time.
X. Min, H. Duan, W. Sun, Y. Zhu, and G. Zhai, “Perceptual video quality assessment: A survey,” Science China Information Sciences (SCIS) , vol. 67, no. 11, p. 211301, 2024
2024
Closest in time.
J. Fu, W. Zhou, Q. Jiang, H. Liu, and G. Zhai, “Vision-language consistency guided multi-modal prompt learning for blind ai generated image quality assessment,” IEEE Signal Processing Letters (SPL) , 2024
2024
Closest in time.
Q. Zheng, Z. Tu, P. C. Madhusudana, X. Zeng, A. C. Bovik, and Y. Fan, “Faver: Blind quality prediction of variable frame rate videos,” Signal Processing: Image Communication (SPIC) , vol. 122, p. 117101, 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.