# High-throughput Onboard Hyperspectral Image Compression with Ground-based CNN Reconstruction

## TL;DR

This paper proposes a high-throughput onboard hyperspectral image compression method that uses prequantization and CNN-based ground reconstruction, balancing low computational complexity with effective rate-distortion performance.

## Contribution

It introduces a novel approach combining prequantization with CNN-based reconstruction to improve onboard hyperspectral image compression efficiency.

## Key findings

- CNN can recover the SNR loss at 2 bits per pixel
- Prequantization with CNN reconstruction achieves high throughput
- Method balances computational simplicity with good image quality

## Abstract

Compression of hyperspectral images onboard of spacecrafts is a tradeoff between the limited computational resources and the ever-growing spatial and spectral resolution of the optical instruments. As such, it requires low-complexity algorithms with good rate-distortion performance and high throughput. In recent years, the Consultative Committee for Space Data Systems (CCSDS) has focused on lossless and near-lossless compression approaches based on predictive coding, resulting in the recently published CCSDS 123.0-B-2 recommended standard. While the in-loop reconstruction of quantized prediction residuals provides excellent rate-distortion performance for the near-lossless operating mode, it significantly constrains the achievable throughput due to data dependencies. In this paper, we study the performance of a faster method based on prequantization of the image followed by a lossless predictive compressor. While this is well known to be suboptimal, one can exploit powerful signal models to reconstruct the image at the ground segment, recovering part of the suboptimality. In particular, we show that convolutional neural networks can be used for this task and that they can recover the whole SNR drop incurred at a bitrate of 2 bits per pixel.