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Mixed Entropy Model Enhanced Residual Attention Network for Remote Sensing Image Compression

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Abstract

In recent years, deep learning has been widely employed in the field of image compression, the most significant of which is the lossy image compression method on the basis of convolutional neural networks. And we study the application of convolutional neural networks to remote sensing image compression. Consequently, we implement an enhanced residual attention module (ERAM) on the primary and hyper coders. ERAM can apply a spatial attention mechanism to generate importance masks for potential features of the original image. And the importance mask can adaptively adjust the bit distribution and allocate more bits to substantial target regions and fewer bits to unimportant background regions. Additionally, ERAM has the capacity to lessen the channel redundancy of potential features. Furthermore, an accurate probability estimate of a potential feature can affect to a great degree the effectiveness of entropy coding and hence image compression. We propose to utilise the discrete mixed Laplace entropy model to parameterize the probability distribution of latent features, which can improve the compression efficiency of latent features in the entropy encoding process. In the field of remote sensing image compression, the experiments demonstrate that the algorithm described in this paper has good subjective effects as well as good objective effects.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 62271336 & 62211530110.

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Authors and Affiliations

  1. College of Electronics and Information Engineering, Sichuan University, Chengdu, 610065, Sichuan, China

    Junjun Gao, Qizhi Teng, Xiaohai He, Zhengxin Chen & Chao Ren

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  1. Junjun Gao
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  2. Qizhi Teng
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  3. Xiaohai He
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  4. Zhengxin Chen
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  5. Chao Ren
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Correspondence to Xiaohai He.

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Gao, J., Teng, Q., He, X. et al. Mixed Entropy Model Enhanced Residual Attention Network for Remote Sensing Image Compression. Neural Process Lett 55, 10117–10129 (2023). https://doi.org/10.1007/s11063-023-11241-0

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