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Hardware Acceleration of Division-Free Quadrature-Based Square Rooting Approach for Near-Lossless Compression of
Amal Altamimi1, Belgacem Ben Youssef2
1Space Technologies Institute, King Abdulaziz City for Science and Technology, P.O. Box 8612, Riyadh 12354, Saudi Arabia.
Sensors (Basel, Switzerland)
|February 26, 2025
Summary
This study introduces a novel FPGA hardware acceleration for near-lossless hyperspectral image compression, significantly improving efficiency for satellite systems. The optimized method achieves high throughput and low power consumption, outperforming existing solutions.
Area of Science:
- Remote Sensing
- Computer Engineering
- Data Compression
Background:
- Hyperspectral imaging generates vast datasets, necessitating efficient compression for satellite systems with limited resources.
- Onboard satellite processing demands real-time capabilities and low power consumption for data handling.
- Existing compression methods struggle to meet the stringent requirements of modern hyperspectral data acquisition.
Purpose of the Study:
- To develop and implement a hardware acceleration for near-lossless hyperspectral image compression.
- To optimize a division-free quadrature-based square rooting method for FPGA implementation.
- To enhance data handling efficiency for onboard satellite systems.
Main Methods:
- A novel FPGA-based hardware acceleration was designed for hyperspectral image compression.
- Division operations in the compression algorithm were replaced with multiplications and geometric series expansion.
- A division-free quadrature-based square rooting method was leveraged for optimization.
Main Results:
- The hardware acceleration achieved a throughput of 1611.77 Mega Samples per second (MSps).
- The system demonstrated a low power requirement of 0.886 Watts on a Cyclone V FPGA.
- An efficiency of 1819.15 MSps/Watt was recorded, surpassing state-of-the-art implementations.
Conclusions:
- The proposed FPGA acceleration offers a highly efficient solution for near-lossless hyperspectral image compression.
- The optimized method significantly reduces power consumption and increases throughput for real-time satellite applications.
- This advancement addresses critical data handling challenges in hyperspectral remote sensing.
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