Abstract
The increase of data rates and data volumes in present remote sensing payload instruments, together with the restrictions
imposed in the downlink connection requirements, represent at the same time a challenge and a must in the field of data
and image compression. This is especially true for the case of hyperspectral images, in which both, reduction of spatial
and spectral redundancy is mandatory. Recently the Consultative Committee for Space Data Systems (CCSDS)
published the Lossless Multispectral and Hyperespectral Image Compression recommendation (CCSDS 123), a
prediction-based technique resulted from the consensus of its members. Although this standard offers a good trade-off
between coding performance and computational complexity, the appearance of future hyperspectral and ultraspectral
sensors with vast amount of data imposes further efforts from the scientific community to ensure optimal transmission to
ground stations based on greater compression rates. Furthermore, hardware implementations with specific features to
deal with solar radiation problems play an important role in order to achieve real time applications. In this scenario, the
Lossy Compression for Exomars (LCE) algorithm emerges as a good candidate to achieve these characteristics. Its good
quality/compression ratio together with its low complexity facilitates the implementation in hardware platforms such as
FPGAs or ASICs.
In this work the authors present the implementation of the LCE algorithm into an antifuse-based FPGA and the
optimizations carried out to obtain the RTL description code using CatapultC, a High Level Synthesis (HLS) Tool.
Experimental results show an area occupancy of 75% in an RTAX2000 FPGA from Microsemi, with an operating
frequency of 18 MHz. Additionally, the power budget obtained is presented giving an idea of the suitability of the
proposed algorithm implementation for onboard compression applications.
