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When it comes to image compression in digital cameras, denoising is traditionally performed prior to compression.
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2020
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2018
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N. Johnston, D. Vincent, D. Minnen, M. Covell, S. Singh, T. Chinen, S. Jin Hwang, J. Shor, and G. Toderici, “Improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks,” in Proc. IEEE/CVF CVPR , 2018, pp. 4385–4393
2018
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2018
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2018
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2019
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2019
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2019
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2019
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2021
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——, “Latent-space scalability for multi-task collaborative intelligence,” in Proc. IEEE ICIP , Sep. 2021, pp. 3562–3566
2021
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M. Testolina, E. Upenik, and T. Ebrahimi, “Towards image denoising in the latent space of learning-based compression,” in Applications of Digital Image Processing XLIV , vol. 11842. SPIE, 2021, pp. 412–422
2021
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K. Zhang, Y. Li, W. Zuo, L. Zhang, L. Van Gool, and R. Timofte, “Plug-and-play image restoration with deep denoiser prior,” IEEE Trans. Pattern Anal. Mach. Intell. , 2021
2021
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ISO/IEC and ITU-T, “Final call for proposals for JPEG AI,” ISO/IEC JTC 1/SC29/WG1 N100095 , 2022
2022
Closest in time.
Z. Guo, Z. Zhang, R. Feng, and Z. Chen, “Causal contextual prediction for learned image compression,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 4, pp. 2329–2341, 2022
2022
Closest in time.
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2022
Closest in time.
H. Choi and I. V. Bajić, “Scalable image coding for humans and machines,” IEEE Trans. Image Process. , vol. 31, pp. 2739–2754, Mar. 2022
2022
Closest in time.
V. Alves de Oliveira, M. Chabert, T. Oberlin, C. Poulliat, M. Bruno, C. Latry, M. Carlavan, S. Henrot, F. Falzon, and R. Camarero, “Satellite image compression and denoising with neural networks,” IEEE Geoscience and Remote Sensing Letters , vol. 19, pp. 1–5, 2022
2022
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2022
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S. Yin, C. Li, Y. Bao, Y. Liang, F. Meng, and W. Liu, “Universal efficient variable-rate neural image compression,” in Proc. IEEE ICASSP , 2022, pp. 2025–2029
2029
Closest in time.