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In this work, we take the first exploration of the recently popular foundation model, i.e., State Space Model/Mamba, in image quality assessment (IQA), aiming at observing and excavating the perception potential in vision Mamba.
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Langley, P · 2000
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
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
Earlier work this paper cites.
Blind image quality assessment: A natural scene statistics approach in the dct domain
Saad, M. A., Bovik, A. C., and Charrier, C · 2012
Earlier work this paper cites.
Convolutional neural networks for no-reference image quality assessment
Kang, L., Ye, P., Li, Y., and Doermann, D · 2014
Earlier work this paper cites.
Massive online crowdsourced study of subjective and objective picture quality
Ghadiyaram, D. and Bovik, A. C · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Blind image quality evaluation using perception based features
Venkatanath, N., Praneeth, D., Bh, M. C., Channappayya, S. S., and Medasani, S. S · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
A probabilistic quality representation approach to deep blind image quality prediction
Zeng, H., Zhang, L., and Bovik, A. C · 2017
Earlier work this paper cites.
Blind image quality estimation via distortion aggravation
Min, X., Zhai, G., Gu, K., Liu, Y., and Yang, X · 2018
Earlier work this paper cites.
Nima: Neural image assessment
Talebi, H. and Milanfar, P · 2018
Earlier work this paper cites.
Kadid-10k: A large-scale artificially distorted iqa database
Lin, H., Hosu, V., and Saupe, D · 2019
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
Earlier work this paper cites.
Perceptual quality assessment of smartphone photography
Fang, Y., Zhu, H., Zeng, Y., Ma, K., and Wang, Z · 2020
Earlier work this paper cites.
Hippo: Recurrent memory with optimal polynomial projections
Gu, A., Dao, T., Ermon, S., Rudra, A., and Ré, C · 2020
Earlier work this paper 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
Earlier work this paper 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
Earlier work this paper cites.
From patches to pictures (paq-2-piq): Mapping the perceptual space of picture quality
Ying, Z., Niu, H., Gupta, P., Mahajan, D., Ghadiyaram, D., and Bovik, A · 2020
Cited alongside, same era.
Blind image quality assessment using a deep bilinear convolutional neural network
Zhang, W., Ma, K., Yan, J., Deng, D., and Wang, Z · 2020
Cited alongside, same era.
Metaiqa: Deep meta-learning for no-reference image quality assessment
Zhu, H., Li, L., Wu, J., Dong, W., and Shi, G · 2020
Cited alongside, same era.
Efficiently modeling long sequences with structured state spaces
Gu, A., Goel, K., and Ré, C · 2021
Cited alongside, same era.
Musiq: Multi-scale image quality transformer
Ke, J., Wang, Q., Wang, Y., Milanfar, P., and Yang, F · 2021
Cited alongside, same era.
Task-driven semantic coding via reinforcement learning
Data-efficient image quality assessment with attention-panel decoder
Qin, G., Hu, R., Liu, Y., Zheng, X., Liu, H., Li, X., and Zhang, Y · 2023
Later among the works it cites.
Re-iqa: Unsupervised learning for image quality assessment in the wild
Saha, A., Mishra, S., and Bovik, A. C · 2023
Later among the works it cites.
Blind image quality assessment via vision-language correspondence: A multitask learning perspective
Zhang, W., Zhai, G., Wei, Y., Yang, X., and Ma, K · 2023
Later among the works it cites.
Quality-aware pre-trained models for blind image quality assessment
Zhao, K., Yuan, K., Sun, M., Li, M., and Wen, X · 2023
Later among the works it cites.
Mambair: A simple baseline for image restoration with state-space model
Guo, H., Li, J., Dai, T., Ouyang, Z., Ren, X., and Xia, S.-T · 2024
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Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B · 2021
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