A 13.3 Gbps 9/7M Discrete Wavelet Transform for CCSDS 122.0-B-1 Image Data Compression on a Space-Grade SRAM FPGA

Abstract

Remote sensing is recognized as a cornerstone monitoring technology. The latest high-resolution and high-speed spaceborne imagers provide an explosive growth in data volume and instrument data rates in the range of several Gbps. This competes with the limited on-board storage resources and downlink bandwidth, making image data compression a mission-critical on-board processing task. The Consultative Committee for Space Data Systems (CCSDS) Image Data Compression (IDC) standard CCSDS-122.0-B-1 is a transform-based 2D image compression algorithm designed specifically for use on-board a space platform. In this paper, we introduce a high-performance architecture for a key-part of the CCSDS-IDC algorithm, the 9/7M Integer Discrete Wavelet Transform (DWT). The proposed parallel architecture achieves 2 samples/cycle while the very deep pipeline enables very high clock frequencies. Moreover, it exploits elastic pipeline principles to provide modularity, latency insensitivity and distributed control. The implementation of the proposed architecture on a Xilinx Kintex Ultrascale XQRKU060 space-grade SRAM FPGA achieves state-of-the-art throughput performance of 831 MSamples/s (13.3 Gbps @ 16bpp) allowing seamless integration with next-generation high-speed imagers and on-board data handling networking technology. To the best of our knowledge, this is the fastest implementation of the 9/7M Integer DWT on a space-grade FPGA, outperforming previous implementations.

Remote Sensing Image Compression Based on the Multiple Prior Information

“…This algorithm achieves better compression performance than the famous PCA + JPEG2000 compression algorithm in rate-distortion performance. Based on DWT, the Consultative Committee for Space Data Systems (CCSDS) also designed a series of international remote sensing image compression standards [20][21][22]. Furthermore, other researchers have also designed efficient compression algorithms based on HEVC and other compression theories, including dictionary learning, compress sensing, and more [4,[23][24][25][26][27].…”