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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Multispectral Image Compression Based on Multiscale Features
verfasst von : Shunmin Zhao, Fanqiang Kong, Kedi Hu, Yuxin Meng
Erschienen in: Communications, Signal Processing, and Systems
Verlag: Springer Singapore
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Abstract
We propose a compression framework based on multiscale features using deep learning technique for complex features of multispectral image. The multiscale feature extraction module is taken as the basic block of the framework to constitute the encoder and the decoder. The encoder is used to extract the dominant features of the input multispectral images and the decoder, which is symmetrical to the encoder, is used to reconstruct multispectral images. The balance of the bit rates and the distortion is implemented by the rate-distortion optimizer in loss function. The encoder and the decoder are trained jointly based on minimizing the mean squared error (MSE). Our proposed framework is compared with JPEG2000 in terms of PSNR and the experimental results validate that our proposed framework outperforms JPEG2000.