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Video quality assessment (VQA) is an important processing task, aiming at predicting the quality of videos in a manner highly consistent with human judgments of perceived quality.
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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 , vol. 30, no. 1, pp. 36–47, 2018
2018
Cited alongside, same era.
K.-Y. Lin and G. Wang, “Hallucinated-iqa: No-reference image quality assessment via adversarial learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018
2018
Cited alongside, same era.
H. Zeng, L. Zhang, and A. C. Bovik, “Blind image quality assessment with a probabilistic quality representation,” in IEEE International Conference on Image Processing (ICIP) , 2018, pp. 609–613
2018
Cited alongside, same era.
B. Chen, L. Zhu, C. Kong, H. Zhu, S. Wang, and Z. Li, “No-reference image quality assessment by hallucinating pristine features,” IEEE Transactions on Image Processing , vol. 31, pp. 6139–6151, 2022
2022
Later among the works it cites.
Z. Liu, J. Ning, Y. Cao, Y. Wei, Z. Zhang, S. Lin, and H. Hu, “Video swin transformer,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 3202–3211
2022
Later among the works it cites.
Z. Tu, H. Talebi, H. Zhang, F. Yang, P. Milanfar, A. Bovik, and Y. Li, “Maxvit: Multi-axis vision transformer,” in European conference on computer vision . Springer, 2022, pp. 459–479
2022
Later among the works it cites.
X. Dong, J. Bao, D. Chen, W. Zhang, N. Yu, L. Yuan, D. Chen, and B. Guo, “Cswin transformer: A general vision transformer backbone with cross-shaped windows,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 12 124–12 134
2022
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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) , January 2022, pp. 1220–1230
2022
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S. Yang, T. Wu, S. Shi, S. Lao, Y. Gong, M. Cao, J. Wang, and Y. Yang, “Maniqa: Multi-dimension attention network for no-reference image quality assessment,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , June 2022, pp. 1191–1200
2022
Later among the works it cites.
W. Zhang, D. Li, C. Ma, G. Zhai, X. Yang, and K. Ma, “Continual learning for blind image quality assessment,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022
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 , vol. 31, pp. 4149–4161, 2022
2022
Later among the works it cites.
Z. Liu, J. Ning, Y. Cao, Y. Wei, Z. Zhang, S. Lin, and H. Hu, “Video swin transformer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022, pp. 3202–3211
2022
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Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A convnet for the 2020s,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 11 966–11 976
2022
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2022
Later among the works it cites.
D. He, Z. Yang, W. Peng, R. Ma, H. Qin, and Y. Wang, “Elic: Efficient learned image compression with unevenly grouped space-channel contextual adaptive coding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022, pp. 5718–5727
2022
Later among the works it cites.
Z. Tu, H. Talebi, H. Zhang, F. Yang, P. Milanfar, A. Bovik, and Y. Li, “Maxim: Multi-axis mlp for image processing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022, pp. 5769–5780
2022
Later among the works it cites.
2022
Later among the works it cites.
J. Rao, C. Qiu, and M. Xiong, “Research on image processing and generative teaching in the context of aigc,” in 2022 3rd International Conference on Information Science and Education (ICISE-IE) , 2022, pp. 20–25
2022
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A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollar, and R. Girshick, “Segment anything,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2023, pp. 4015–4026
2023
Later among the works it cites.
H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” in Advances in Neural Information Processing Systems , A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, Eds., vol. 36. Curran Associates, Inc., 2023, pp. 34 892–34 916. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2023/file/6dcf277ea32ce3288914faf369fe6de0-Paper-Conference.pdf
2023
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2023
Later among the works it cites.
R. Yang and S. Mandt, “Lossy image compression with conditional diffusion models,” in Advances in Neural Information Processing Systems , A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, Eds., vol. 36. Curran Associates, Inc., 2023, pp. 64 971–64 995. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2023/file/ccf6d8b4a1fe9d9c8192f00c713872ea-Paper-Conference.pdf
2023
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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,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 2, pp. 2555–2563, Jun. 2023. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/25353
2023
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2023
Later among the works it cites.
H. Wu, E. Zhang, L. Liao, C. Chen, J. Hou, A. Wang, W. Sun, Q. Yan, and W. Lin, “Towards explainable in-the-wild video quality assessment: a database and a language-prompted approach,” in Proceedings of the 31st ACM International Conference on Multimedia , 2023, pp. 1045–1054
2023
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2023
Later among the works it cites.
D. Danier, F. Zhang, and D. R. Bull, “Bvi-vfi: A video quality database for video frame interpolation,” IEEE Transactions on Image Processing , vol. 32, pp. 6004–6019, 2023
2023
Later among the works it cites.
H. Wu, E. Zhang, L. Liao, C. Chen, J. Hou, A. Wang, W. Sun, Q. Yan, and W. Lin, “Towards explainable in-the-wild video quality assessment: A database and a language-prompted approach,” in Proceedings of the 31st ACM International Conference on Multimedia , ser. MM ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 1045–1054. [Online]. Available: https://doi.org/10.1145/3581783.3611737
2023
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——, “Exploring video quality assessment on user generated contents from aesthetic and technical perspectives,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2023, pp. 20 144–20 154
2023
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Z. Zhang, W. Wu, W. Sun, D. Tu, W. Lu, X. Min, Y. Chen, and G. Zhai, “Md-vqa: Multi-dimensional quality assessment for ugc live videos,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2023, pp. 1746–1755
2023
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J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “Making video quality assessment models robust to bit depth,” IEEE Signal Processing Letters , vol. 30, pp. 488–492, 2023
2023
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2023
Later among the works it cites.
Y. Liu, L. Li, S. Ren, R. Gao, S. Li, S. Chen, X. Sun, and L. Hou, “Fetv: A benchmark for fine-grained evaluation of open-domain text-to-video generation,” in Advances in Neural Information Processing Systems , A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, Eds., vol. 36. Curran Associates, Inc., 2023, pp. 62 352–62 387. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2023/file/c481049f7410f38e788f67c171c64ad5-Paper-Datasets_and_Benchmarks.pdf
2023
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J. Xu, X. Liu, Y. Wu, Y. Tong, Q. Li, M. Ding, J. Tang, and Y. Dong, “Imagereward: Learning and evaluating human preferences for text-to-image generation,” in Advances in Neural Information Processing Systems , A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, Eds., vol. 36. Curran Associates, Inc., 2023, pp. 15 903–15 935. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2023/file/33646ef0ed554145eab65f6250fab0c9-Paper-Conference.pdf
2023
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Y. Kirstain, A. Polyak, U. Singer, S. Matiana, J. Penna, and O. Levy, “Pick-a-pic: An open dataset of user preferences for text-to-image generation,” in Advances in Neural Information Processing Systems , A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, Eds., vol. 36. Curran Associates, Inc., 2023, pp. 36 652–36 663. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2023/file/73aacd8b3b05b4b503d58310b523553c-Paper-Conference.pdf
2023
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Z. Zhang, C. Li, W. Sun, X. Liu, X. Min, and G. Zhai, “A perceptual quality assessment exploration for aigc images,” in 2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) , 2023, pp. 440–445
2023
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OpenAI, “GPT-4 technical report,” https://openai.com/research/gpt-4 , 2023
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 , vol. 17, no. 6, pp. 1178–1192, 2023
2023
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S. Sun, T. Yu, J. Xu, W. Zhou, and Z. Chen, “Graphiqa: Learning distortion graph representations for blind image quality assessment,” IEEE Transactions on Multimedia , vol. 25, pp. 2912–2925, 2023
2023
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G. Qin, R. Hu, Y. Liu, X. Zheng, H. Liu, X. Li, and Y. Zhang, “Data-efficient image quality assessment with attention-panel decoder,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 2, pp. 2091–2100, Jun. 2023
2023
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S. Su, Q. Yan, Y. Zhu, J. Sun, and Y. Zhang, “From distortion manifold to perceptual quality: a data efficient blind image quality assessment approach,” Pattern Recognition , vol. 133, p. 109047, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0031320322005271
2023
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R. Ma, Q. Wu, K. N. Ngan, H. Li, F. Meng, and L. Xu, “Forgetting to remember: A scalable incremental learning framework for cross-task blind image quality assessment,” IEEE Transactions on Multimedia , vol. 25, pp. 8817–8827, 2023
2023
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Z. Wang, Q. Jiang, S. Zhao, W. Feng, and W. Lin, “Deep blind image quality assessment powered by online hard example mining,” IEEE Transactions on Multimedia , vol. 25, pp. 4774–4784, 2023
2023
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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) , June 2023, pp. 5846–5855
2023
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N. C. Babu, V. Kannan, and R. Soundararajan, “No reference opinion unaware quality assessment of authentically distorted images,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , January 2023, pp. 2459–2468
2023
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K. Zhao, K. Yuan, M. Sun, M. Li, and X. Wen, “Quality-aware pre-trained models for blind image quality assessment,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2023, pp. 22 302–22 313
2023
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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) , June 2023, pp. 14 071–14 081
2023
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J. Hou, W. Lin, Y. Fang, H. Wu, C. Chen, L. Liao, and W. Liu, “Towards transparent deep image aesthetics assessment with tag-based content descriptors,” IEEE Transactions on Image Processing , pp. 1–1, 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.
2023
Later among the works it cites.
H. Wu, C. Chen, L. Liao, J. Hou, W. Sun, Q. Yan, J. Gu, and W. Lin, “Neighbourhood representative sampling for efficient end-to-end video quality assessment,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 12, pp. 15 185–15 202, 2023
2023
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X. Jiang, W. Tan, T. Tan, B. Yan, and L. Shen, “Multi-modality deep network for extreme learned image compression,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 1, pp. 1033–1041, Jun. 2023
2023
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X. Mao, Y. Liu, F. Liu, Q. Li, W. Shen, and Y. Wang, “Intriguing findings of frequency selection for image deblurring,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 2, pp. 1905–1913, Jun. 2023
2023
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S.-K. Chen, H.-L. Yen, Y.-L. Liu, M.-H. Chen, H.-N. Hu, W.-H. Peng, and Y.-Y. Lin, “Learning continuous exposure value representations for single-image hdr reconstruction,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2023, pp. 12 990–13 000
2023
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K. Panetta, S. K. K. M., S. P. Rao, and S. S. Agaian, “Deep perceptual image enhancement network for exposure restoration,” IEEE Transactions on Cybernetics , vol. 53, no. 7, pp. 4718–4731, 2023
2023
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2023
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A. F. Di Natale, M. E. Simonetti, S. La Rocca, and E. Bricolo, “Uncanny valley effect: A qualitative synthesis of empirical research to assess the suitability of using virtual faces in psychological research,” Computers in Human Behavior Reports , vol. 10, p. 100288, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2451958823000210
2023
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Q. Ye, H. Xu, J. Ye, M. Yan, A. Hu, H. Liu, Q. Qian, J. Zhang, and F. Huang, “mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 13 040–13 051
2024
Closest in time.
H. Wu, Z. Zhang, E. Zhang, C. Chen, L. Liao, A. Wang, K. Xu, C. Li, J. Hou, G. Zhai, G. Xue, W. Sun, Q. Yan, and W. Lin, “Q-instruct: Improving low-level visual abilities for multi-modality foundation models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 25 490–25 500
2024
Closest in time.
C. He, Q. Zheng, R. Zhu, X. Zeng, Y. Fan, and Z. Tu, “Cover: A comprehensive video quality evaluator,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , June 2024, pp. 5799–5809
2024
Closest in time.
K. Yuan, H. Liu, M. Li, M. Sun, M. Sun, J. Gong, J. Hao, C. Zhou, and Y. Tang, “Ptm-vqa: Efficient video quality assessment leveraging diverse pretrained models from the wild,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 2835–2845
2024
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2024
Closest in time.
J. P. Ebenezer, Z. Shang, Y. Chen, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “HDR or SDR? a subjective and objective study of scaled and compressed videos,” IEEE Transactions on Image Processing , 2024
2024
Closest in time.
Y. Lu, X. Li, Y. Pei, K. Yuan, Q. Xie, Y. Qu, M. Sun, C. Zhou, and Z. Chen, “Kvq: Kwai video quality assessment for short-form videos,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 25 963–25 973
2024
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Z. Shang, J. P. Ebenezer, A. K. Venkataramanan, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “A study of subjective and objective quality assessment of hdr videos,” IEEE Transactions on Image Processing , vol. 33, pp. 42–57, 2024
2024
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Y. Liu, X. Cun, X. Liu, X. Wang, Y. Zhang, H. Chen, Y. Liu, T. Zeng, R. Chan, and Y. Shan, “Evalcrafter: Benchmarking and evaluating large video generation models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 22 139–22 149
2024
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Z. Huang, Y. He, J. Yu, F. Zhang, C. Si, Y. Jiang, Y. Zhang, T. Wu, Q. Jin, N. Chanpaisit, Y. Wang, X. Chen, L. Wang, D. Lin, Y. Qiao, and Z. Liu, “Vbench: Comprehensive benchmark suite for video generative models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 21 807–21 818
2024
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2024
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2024
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2024
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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 Artificial Intelligence , L. Fang, J. Pei, G. Zhai, and R. Wang, Eds. Singapore: Springer Nature Singapore, 2024, pp. 46–57
2024
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2024
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C. Li, Z. Zhang, H. Wu, W. Sun, X. Min, X. Liu, G. Zhai, and W. Lin, “Agiqa-3k: An open database for ai-generated image quality assessment,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 34, no. 8, pp. 6833–6846, 2024
2024
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C. Li, T. Kou, Y. Gao, Y. Cao, W. Sun, Z. Zhang, Y. Zhou, Z. Zhang, H. Wu, W. Zhang, X. Liu, X. Min, and Z. Guangtao, “AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , 2024
2024
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2024
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A. K. Venkataramanan, C. Stejerean, I. Katsavounidis, and A. C. Bovik, “One transform to compute them all: Efficient fusion-based full-reference video quality assessment,” IEEE Transactions on Image Processing , vol. 33, pp. 509–524, 2024
2024
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C. Chen, J. Mo, J. Hou, H. Wu, L. Liao, W. Sun, Q. Yan, and W. Lin, “Topiq: A top-down approach from semantics to distortions for image quality assessment,” IEEE Transactions on Image Processing , vol. 33, pp. 2404–2418, 2024
2024
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H. Zhu, B. Chen, L. Zhu, P. Chen, L. Song, and S. Wang, “Video quality assessment for spatio-temporal resolution adaptive coding,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 34, no. 7, pp. 6403–6415, 2024
2024
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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 , vol. 122, 2024
2024
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N.-H. Shin, S.-H. Lee, and C.-S. Kim, “Blind image quality assessment based on geometric order learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 12 799–12 808
2024
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H. Li, L. Liao, C. Chen, X. Fan, W. Zuo, and W. Lin, “Continual learning of blind image quality assessment with channel modulation kernel,” IEEE Transactions on Circuits and Systems for Video Technology , pp. 1–1, 2024
2024
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W. Zhang, K. Ma, G. Zhai, and X. Yang, “Task-specific normalization for continual learning of blind image quality models,” IEEE Transactions on Image Processing , vol. 33, pp. 1898–1910, 2024
2024
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A. Shukla, A. Upadhyay, S. Bhugra, and M. Sharma, “Opinion unaware image quality assessment via adversarial convolutional variational autoencoder,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , January 2024, pp. 2153–2163
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L. Agnolucci, L. Galteri, M. Bertini, and A. Del Bimbo, “Arniqa: Learning distortion manifold for image quality assessment,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , January 2024, pp. 189–198
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2024
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A. De Decker, J. De Cock, P. Lambert, and G. Van Wallendael, “No-reference vmaf: A deep neural network-based approach to blind video quality assessment,” IEEE Transactions on Broadcasting , pp. 1–0, 2024
2024
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J. Yan, L. Wu, Y. Fang, X. Liu, X. Xia, and W. Liu, “Video quality assessment for online processing: From spatial to temporal sampling,” IEEE Transactions on Circuits and Systems for Video Technology , pp. 1–1, 2024
2024
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W. Shen, M. Zhou, X. Wei, H. Wang, B. Fang, C. Ji, X. Zhuang, J. Wang, J. Luo, H. Pu, X. Huang, S. Wang, H. Cao, Y. Feng, T. Xiang, and Z. Shang, “A blind video quality assessment method via spatiotemporal pyramid attention,” IEEE Transactions on Broadcasting , vol. 70, no. 1, pp. 251–264, 2024
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Y. Liu, Y. Quan, G. Xiao, A. Li, and J. Wu, “Scaling and masking: A new paradigm of data sampling for image and video quality assessment,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 4, pp. 3792–3801, Mar. 2024. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/28170
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S. Mitra and R. Soundararajan, “Knowledge guided semi-supervised learning for quality assessment of user generated videos,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 5, pp. 4251–4260, Mar. 2024
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W. Wen, M. Li, Y. Zhang, Y. Liao, J. Li, L. Zhang, and K. Ma, “Modular blind video quality assessment,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 2763–2772
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Y. Mi, Y. Li, Y. Shu, and S. Liu, “Ze-fesg: A zero-shot feature extraction method based on semantic guidance for no-reference video quality assessment,” in ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2024, pp. 3640–3644
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Y. Zhong, J. Liu, X. Huang, J. Liu, Y. Fan, and M. Wu, “Cdcnet: A fast and lightweight dehazing network with color distortion correction,” in ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2024, pp. 3020–3024
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Z. Zhang, S. Zhang, R. Wu, W. Zuo, R. Timofte, X. Xing, H. Park, S. Song, C. Kim, X. Kong et al. , “Ntire 2024 challenge on bracketing image restoration and enhancement: Datasets methods and results,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , June 2024, pp. 6153–6166
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J. Li, B. Li, Z. Tu, X. Liu, Q. Guo, F. Juefei-Xu, R. Xu, and H. Yu, “Light the night: A multi-condition diffusion framework for unpaired low-light enhancement in autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 15 205–15 215
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R. Zhu, S. Xu, P. Liu, S. Li, Y. Lu, D. Niu, Z. Liu, Z. Meng, Z. Li, X. Chen, and Y. Fan, “Zero-shot structure-preserving diffusion model for high dynamic range tone mapping,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 26 130–26 139
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P. Shyam and H. Yoo, “Pair: Perception aided image restoration for natural driving conditions,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , January 2024, pp. 7459–7470
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L. Liao, K. Xu, H. Wu, C. Chen, W. Sun, Q. Yan, C.-C. Jay Kuo, and W. Lin, “Blind video quality prediction by uncovering human video perceptual representation,” IEEE Transactions on Image Processing , vol. 33, pp. 4998–5013, 2024
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2024
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Z. Tu, C.-J. Chen, L.-H. Chen, Y. Wang, N. Birkbeck, B. Adsumilli, and A. C. Bovik, “Regression or classification? new methods to evaluate no-reference picture and video quality models,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2021, pp. 2085–2089
2089
Closest in time.