Abstract
Multispectral images contain features with complex information and high spatial and inter-spectral redundancy. To better achieve multispectral image compression, an end-to-end multispectral image compression framework based on wavelet transform is proposed in this paper. In this method, the forward transform uses the boosted wavelet method and then the master encoder performs the mapping transform on the subbands after wavelet transform to obtain the latent features. The entropy model further extracts the potential features edge information for modeling to assist the entropy coding and decoding process of subbands, and finally recovered into multispectral images. The experimental results show that our method is feasible.
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References
Ballé J, Laparra V, Simoncelli EP (2017) End-to-end optimized image compression. Int. Confer. Learn. Represent.
Toderici G, O'Malley SM, Hwang SJ et al (2016) Variable rate image compression with recurrent neural networks. In: ICLR2016. CoRR abs/1511.06085: n. pag
Toderici G, Vincent D, Johnston N et al (2017) Full resolution image compression with recurrent neural networks. In: IEEE CVPR2017 1, pp 5435–5443. https://doi.org/10.1109/CVPR.2017.577
Li J, Liu Z (2019) Multispectral transforms using convolution neural networks for remote sensing multispectral image compression. Remote Sens 11(7):759
Ballé J, Laparra V, Simoncelli EP (2017) End-to-end optimized image compression. In: 5th International conference on learning representations. ICLR 2017
Ballé J, Minnen D, Singh S, Hwang SJ, Johnston N (2018) Variational image compression with a scale hyperprior
Minnen D, Ballé J, Toderici GD (2018) Joint autoregressive and hierarchical priors for learned image compression. In: Advances in neural information processing systems, vol 31
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Xu, D., Kong, F., Ren, G., Tang, J., Huang, M. (2024). End-To-End Wavelet-Based Compression of Multispectral Images. In: Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2023. Lecture Notes in Electrical Engineering, vol 1033. Springer, Singapore. https://doi.org/10.1007/978-981-99-7502-0_35
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DOI: https://doi.org/10.1007/978-981-99-7502-0_35
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