CCSDS 2007, October 1-5, Darmstadt, Germany. Developments of CCSDS Data Compression for Space Exploration in CSSAR/CAS. Center for Space Science and Applied Research Chinese Academy of Sciences firstname.lastname@example.org. Zhongwei Zhang. Contents.
CCSDS 2007, October 1-5, Darmstadt, Germany
Center for Space Science and Applied Research Chinese Academy of Sciences email@example.com
1. Science data compression
● In the ongoing project of Deep Space Exploration, there is a requirement to compress science data from several payloads, such as the Plasma(Ion) probe, Obscuration probe, Radial spectrum sensor, and other some probes. Due to the very limited downlink channel bandwidth, a lossless data compression module is the key to successfully complete the mission. So a lossless data compression software is developed for the mission, and the algorithm fully comply with the CCSDS Recommend: CCSDS 121.0-B-1.
1. Science data compression-continue
● The lossless data compression software performance:
Some emulational data source is provided by the scientists. The data compression algorithm is tested with these data, and the scientists are satisfied with the performance of the algorithm. The software will proceed the data from more than 5 different payloads for this mission.
● For all the emulational data resource, the more than 2.0 compression rate is gained by our algorithm software.
Compression rate = (original data size) / (compressed data size)
2. Image compression
● In the same mission, there is a data compression requirement to the payload: CCD camera, so an image compression algorithm is developed in May 2006, and the algorithm fully comply with the CCSDS Recommend: CCSDS 122.0-B-1.
● The image compression algorithm is tested with the image source which is provided by the CCDS website. At the test conditions of the lossless and the compression rate less than 4.0, the image compression algorithm can gain the similar compression PSNR values, compared with the results of the Dr. Yeh’s paper (published in 2005). But it is found that there always has about 2.0 dB difference when the compression rate is more than 4.0.
● The CCSDS image compression WG provided a reference software this year. By comparing with the reference software, the performance of our algorithm were improved.
● The image compression software module will proceed the data from the CCD camera with the compression factors: the lossless compression and the compression rate from 1 to 64. The compression factors would be decided by the ground tele-control commands.
● The testing problem:
The image compression software is been modifying for the engineering applications. On the condition of all the payloads be on line, the encoder and decoder work well, and no any problems are found. But a fatal error occurs when the system are tested on the wireless network. It is found that the decoder can not deal with the errors which are maybe provoked by the following reason:
1. Channel Bit error
2. Package loss
3. The system network misses synchronization
● CCSDS 122.0-B-1 not provide the recommended error control scheme.
● Hardware development for CCSDS 122.0-B-1
The hardware system are been developing on FPGA for that. The right picture is the developing board, which is a Xilinx FPGA developing system for the multimedia applications, and the FPGA chip is V2-2000.
● Current status:
1. all the VHDL codes of the algorithm are completed.
2. And the coder can implement on the developing board and output the compressed stream. The coded stream is testified by the decoder software on the PC.
3. But its processing speed is very low,
4. Now, the parallel computation modes are designed to improve the hardware implementation speed.
● A research item: How to keep a consistent quality for the hyperspectral image compression.
For the hyperspectral image compression, the accurate rate control maybe provokes the quality fluctuation between the successive reconstructed images, especially for the high compression rate. If there is a very severe content variation between the continuous images, the severe quality fluctuation will occurs for those images after the compression processing. Under the situation, the shape of the spectral curve will be distorted by the compression processing. The distorted shape of the spectral curves maybe mislead the decisions of some users, such as the geologists, the mineral resources explorers. That is bad for the MHDC. So a restriction condition must be added for the rate control algorithm, which can limit the image quality fluctuation scope during the data compression. That is to say, the rate control algorithm should be able to do a tradeoff between the rate control and the quality fluctuation restriction. So it is expected to keep a consistent image quality or keep a similar spectrum shape for MHDC.