End-To-End Wavelet-Based Compression of Multispectral Images

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.