An End-to-End Multispectral Image Compression Network Based on Weighted Channels
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Publisher
Springer Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-15-8411-4_58
Reference8 articles.
1. Gelli G, Poggi G (1999) Compression of multispectral images by spectral classification and transform coding. IEEE Trans Image Process 8(4):476–489. https://doi.org/10.1109/83.753736
2. Toderici G, O’Malley SM, Hwang SJ et al (2016) Variable rate image compression with recurrent neural networks. In: ICLR2016. Available from https://arxiv.org/abs/1511.06085. Cited 23 Apr 2020
3. Toderici G, Vincent D, Johnston N et al (2017) Full resolution image compression with recurrent neural networks. CVPR 2017:5435–5443. https://doi.org/10.1109/cvpr.2017.557
4. Jiao SM, Jin Z, Chang CL et al (2018) Compression of phase-only holograms with JPEG standard and deep learning. Appl Sci 8(8):1258. https://doi.org/10.3390/app8081258
5. Mahoney M (2014) The ZPAQ open standard format for highly compressed data - level 2. Available from https://mattmahoney.net/dc/zpaq204.pdf. Cited 23 Apr 2020
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