面向空间天文观测的序列图像无损压缩算法

孙建伟; 薛长斌; 郑铁; 张忠伟

doi:10.11728/cjss2019.06.847

面向空间天文观测的序列图像无损压缩算法

doi: 10.11728/cjss2019.06.847 cstr: 32142.14.cjss2019.06.847

1. 中国科学院国家空间科学中心北京 100190;
2. 中国科学院大学北京 100049

基金项目:

中国科学院空间科学战略性先导科技专项I!I期项目资助（XDA15320100）

详细信息

作者简介:

孙建伟,E-mail:sunjianwei729@163.com

中图分类号: V524;TP391
计量
- 文章访问数: 1136
- HTML全文浏览量: 85
- PDF下载量: 100
- 被引次数:
  0(来源:Crossref)
  
  0(来源:其他)
出版历程
- 收稿日期: 2018-11-06
- 修回日期: 2019-04-04
- 刊出日期: 2019-11-15

Sequential Imagery Lossless Compression Algorithm for Space Astronomical Observation

1 National Science Space Center, Chinese Academy of Sciences, Beijing 100190;
2 University of Chinese Academy of Sciences, Beijing 100049

摘要

摘要: 空间天文观测任务会获得大量天文图像.对定点天体连续观测得到的序列天文图像具有时间及空间冗余较高的特点.为了减少序列天文图像的存储与传输的数据量，保证序列天文图像的完整性，满足科学目标的任务需求，需要对其进行无损压缩.本文提出了一种利用帧内压缩与改进的帧间压缩相结合的无损压缩算法，将序列天文图像的第一帧进行JPEG-LS帧内无损压缩编码，其余帧进行改进的帧间无损压缩编码，从而有效去除序列天文图像的时间及空间冗余，提高序列天文图像的压缩比.经过试验测试，改进后的帧间压缩效果优于帧内压缩效果，改进后的帧间压缩时间少于帧内压缩时间.结果表明，该算法简单且高效，适用于对序列天文图像的无损压缩.
- 序列图像 /
- JPEG-LS /
- 帧内压缩 /
- 帧间压缩
Abstract: In astronomical observation missions, a large number of sequential imagery is obtained. They are produced by fixpoint photography in a period of time, and all have some common features with high resolution and high time-space redundancy. The large volume of such imagery challenges on-board transmission and storage mission. Hence, an algorithm based on inter-frame prediction is proposed in order to reduce the images' bitrates. It includes the intra-frame compression algorithm and an improved inter-frame compression algorithm. The JPEG-LS is adopted to encoding the first image as the intra-frame compression algorithm. Then the other images are compressed by the inter-frame compression algorithm. First, the next image is predicted by its previous image, and then the prediction residuals are transmitted to JPEG-LS predictor for refining. Finally, the refined residuals are encoded by arithmetic encoder. Arithmetic coding does not require encoding each pixel like Huffman coding, and it is closer to the entropy of the image. The experiment result proves that the proposed algorithm promotes the compression performance, and it has lower complexity and less compression time comparing with the traditional JPEG-LS. The proposed algorithm is suitable for real-time onboard compression of astronomical observation sequential imagery.
- Sequential imagery /
- JPEG-LS /
- Intra-frame compression /
- Inter-frame compression

HTML全文

参考文献(16)

[1]	STARCK J L, MURTAGH F. Astronomical image and data analysis[M]. New York:Springer Science & Business Media, 2007
[2]	WEINBERGER M J, SEROUSSI G, SAPIRO G. The LOCO-I lossless image compression algorithm:principles and standardization into JPEG-LS[J]. IEEE Trans. Image Process., 2000, 9(8):1309-1324
[3]	CANDES E J, ROMBERG J, TAO T. Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information[J]. IEEE Trans. Inf. Theory, 2006, 52(2):489-509
[4]	DONOHO D L. Compressed sensing[J]. IEEE Trans. Inf. Theory, 2006, 52(4):1289-1306
[5]	THIENPHRAPA P, DONG J. A robust transmission system for astronomical images over error-prone links[C]//Proceedings SPIE, 2005, 6015:60150J
[6]	BOBIN J, STARCK J L, OTTENSAMER R. Compressed sensing in astronomy[J]. IEEE J. Sel. Top. Sign. Process., 2008, 2(5):718-726
[7]	STARCK J L, BOBIN J. Astronomical data analysis and sparsity:From wavelets to compressed sensing[J]. Proc. IEEE, 2010, 98(6):1021-1030
[8]	LI Long, DAI Hongbing, XU Jun. Application research of image compression based on wavelet transform in astronomical remote observation[J]. Astron. Res. Technol.-Natl. (Astron. Observ.), 2008, 5(4):380-385
[9]	ZHU Fugui. Research and Implementation of Lossless Compression Algorithm for Astronomical Images[D]. Kunming:Kunming University of Science and Technology, 2009(朱富贵. 天文图像无损压缩算法研究与实现[D]. 昆明:昆明理工大学, 2009)
[10]	LI Yang. Research of Compressed Sensing in Astronomy Images[D]. Nanjing:Nanjing University of Information Science & Technology, 2014(李洋. 压缩感知在天文图像中的应用研究[D]. 南京:南京信息工程大学, 2014)
[11]	WEINBERGER M J, SEROUSSI G, SAPIRO G. LOCO-I:A low complexity, context-based, lossless image compression algorithm[C]//Data Compression Conference, 1996. DCC'96 Proceedings. Snowbird:IEEE, 1996:140-149
[12]	GOLOMB S. Run-length encodings (Corresp.)[J]. IEEE Trans. Inf. Theory, 1966, 12(3):399-401
[13]	RICE R F. Some practical universal noiseless coding techniques[R]. California:Jet Propulsion Laboratory, 1991
[14]	HUFFMAN D A. A method for the construction of minimum-redundancy codes[J]. Proc. IRE, 1952, 40(9):1098-1101
[15]	GONZALEZ R C, WOODS R E. Digital image processing[M]. 3rd ed. Beijing:Publishing House of Electronics Industry, 2001
[16]	SUN Jianwei, ZHANG Zhongwei, ZHENG Tie, XUE Changbin, CHEN Cong. Design of lossless compression system for CCSDS on-board data based on FPGA[J]. Chin. J. Space Sci., 2019, 39(5):694-700(孙建伟, 张忠伟, 郑铁, 薛长斌, 陈聪. 基于FPGA的CCSDS星载数据无损压缩系统设计. 空间科学学报, 2019, 39(5):694-700)