In aerospace, onboard image sensors generate extensive data volumes, resulting in slow data transmission, high energy consumption, and costly infrastructure. Optimizing raw image data workflows for satellite imagery is crucial for advancing Earth Observation and maximizing AI/ML potential.
Conventional lossless compression is slow and provides a limited compression ratio, failing to address scalability concerns. Meanwhile, visually lossless algorithms jeopardize the robustness of ML models in the context of AI-driven analysis of Earth Observation image data, especially in applications like environmental monitoring, geospatial analysis, and aerial agriculture data.
The onboard data volume encounters hardware bottlenecks, forcing a trade-off between preserving raw image quality and achieving a high frame rate
Onboard systems face challenges with limited power and growing data volumes, impacting data management efficiency and energy consumption
Transmitting data from satellite to ground proves to be costly and slow, limiting the amount of data downloaded on the ground
Ample data storage, whether on-board or on the ground, drives up mission costs.
End-users face long transfer time to access critical Earth observation image data. Applying visual lossless compression techniques impacts the results of their AI-based analysis and predictions by introducing compression artefacts only perceived by AI.
Jetraw Core stands as a high-efficiency raw image compression solution. It seamlessly integrates into FPGA IP Core or functions as software within data centers. This integration significantly enhances the transmission speed of raw image data while preserving the utmost data quality, thereby ensuring scalable and dependable AI analysis.
Jetraw Core addresses the challenges associated with every step of the aerospace image workflow. It excels in energy efficiency and infrastructure optimization, leading to substantial advantages for both satellite manufacturers and Earth observation data users.