https://doi.org/10.1140/epjp/s13360-025-06499-3
Regular Article
HFPCE: hierarchical feature permutation-compression encoding for remote sensing image with 2D-HTMGM and BP-BCS
1
School of Software, Taiyuan University of Technology, 030024, Taiyuan, China
2
College of Computer Science and Technology, Taiyuan University of Technology, 030024, Taiyuan, China
3
School of Mathematics, Chongqing Normal University, 400700, Chongqing, China
Received:
13
March
2025
Accepted:
29
May
2025
Published online:
28
June
2025
In response to the existing algorithms’ inability to balance the compression reconstruction quality and computational efficiency for remote sensing images, this paper proposes a combined lossy and lossless compression and encryption algorithm. To enhance randomness and security, a novel 2D hyperbolic tangent memristive gaussian map is designed, demonstrating higher complexity and stronger chaotic behavior compared to existing chaotic systems. Furthermore, this paper reduces the information redundancy between color image channels by using adaptive inter-channel predictive coding. The image is divided into high-frequency and low-frequency parts through integer wavelet transform, which are then compressed and encrypted via lossy and lossless schemes, respectively. In the lossy compression algorithm, an improved Gray coding is integrated into the neural network compression method to achieve efficient joint compression and encryption. Finally, an adaptive block parallel and bidirectional cyclic shift diffusion method is proposed to enhance the security of the algorithm. Experimental results show that under compression ratio (CR) of 0.5, the lossy compression achieves PSNR of 49.5117 dB and an SSIM of 0.9781. Meanwhile, the lossless part achieves Bpp of 2.9298. Compared to existing approaches, the proposed algorithm exhibits better performance in both compression efficiency and security robustness.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
