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April 10, 2026Scientific Reports0 citationsOpen Access

Unified FPGA framework for secure satellite image transmission: adaptive orthogonal moments with integrated confusion-diffusion cryptography

IMIsmail MchichouMTMohamed Amine TahiriHAHicham Amakdouf

Key Points

  • The goal is to create an efficient FPGA system that combines image compression and encryption for satellite images.
  • Developed a unified FPGA framework for adaptive orthogonal moments on Xilinx ZedBoard Zynq-7000.
  • Implemented four families of orthogonal moments with adaptive selection based on algorithm accuracy.
  • Applied a confusion-diffusion chaotic encryption technique to the coefficients for enhanced security.
  • Achieved PSNR varying between 27-36 dB for Sentinel-2 images at 4.6:1 compression.
  • Recorded a latency of 728 ns during image processing.
  • Demonstrated high security with an entropy of 7.99 bits.

Abstract

Satellite image transmission systems face a dual challenge: efficient compression and robust encryption. Existing approaches treat these requirements sequentially, resulting in doubled latency and energy overhead, while no FPGA implementation of orthogonal moments exists in the literature. This work presents the first unified FPGA framework transforming adaptive orthogonal moments into dual compression-encryption vectors. The system implements four moment families (Charlier, Hahn, Meixner, Krawtchouk) on Xilinx ZedBoard Zynq-7000, with real-time adaptive selection (93.8% accuracy) and confusion-diffusion chaotic encryption applied directly to coefficients. Three major contributions are demonstrated: (i) first FPGA hardware architecture for orthogonal moments via hybrid PC/FPGA strategy, (ii) adaptive multi-family framework optimized for satellite energy constraints, (iii) unified pipeline eliminating intermediate stages. Implemented at 125 MHz on ZedBoard, the system achieves PSNR of 27-36 dB for Sentinel-2 images of Formula: see text pixels (4.6:1 compression), 728 ns latency, and robust security (7.99 bits entropy, correlations Formula: see text).

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Cite This Study

Mchichou et al. (2026) studied this question.

synapsesocial.com/papers/69d892d16c1944d70ce04006https://doi.org/10.1038/s41598-026-43728-w
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