Abstract
Multispectral images have rich spatial and spectral information which contain great application superiority. Therefore, effective compression of multispectral images is crucial. This paper proposes an end-to-end network architecture based on prediction networks to complete multispectral image compression tasks. Specifically, the feature extraction module can extract spatial and spectral information effectively and reduce information redundancy. The prediction module is able to predict the original image and obtain the residual one according to the reference spectral image and the extracted features. All modules are jointly optimized by a single loss function. The experimental results show that proposed compression framework outperforms conventional methods, including JPEG2000 and 3D-SPIHT.
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References
Luo M, Bu Y, Xu JH et al (2017) (2017) Optical element surface defect measurement based on multispectral technique. Chin J Laser 01:204–213
Wallace GK (1991) The JPEG still picture compression standard. Commun ACM 34(4):30–44
Skodras A, Christopoulos C, Ebrahimi T (2001) The JPEG 2000 still image compression standard. IEEE Sig Process Mag 18(5):36–58
Toderici G, O'Malley SM, Hwang SJ et al (2015) Variable rate image compression with recurrent neural networks. arXiv preprint arXiv:1511.06085
Kong FQ, Zhou YB, Shen Q et al (2019) An end-to-end multispectral image compression method based on convolutional neural network. Chin J Lasers 46(10):285–293
Lu G, Ouyang W, Xu D et al (2019) Dvc: an end-to-end deep video compression framework. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 11006–11015
Ranjan A, Black MJ (2017) Optical flow estimation using a spatial pyramid network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4161–4170
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Huang, M., Kong, F., Tang, J., Ren, G., Xu, D. (2024). Multispectral Image Compression Based on Prediction Network. 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_23
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DOI: https://doi.org/10.1007/978-981-99-7502-0_23
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