Volume 39 Issue 6
Nov.  2019
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SUN Jianwei, XUE Changbin, ZHENG Tie, ZHANG Zhongwei. Sequential Imagery Lossless Compression Algorithm for Space Astronomical Observation[J]. Journal of Space Science, 2019, 39(6): 847-852. doi: 10.11728/cjss2019.06.847
Citation: SUN Jianwei, XUE Changbin, ZHENG Tie, ZHANG Zhongwei. Sequential Imagery Lossless Compression Algorithm for Space Astronomical Observation[J]. Journal of Space Science, 2019, 39(6): 847-852. doi: 10.11728/cjss2019.06.847

Sequential Imagery Lossless Compression Algorithm for Space Astronomical Observation

doi: 10.11728/cjss2019.06.847
  • Received Date: 2018-11-06
  • Rev Recd Date: 2019-04-04
  • Publish Date: 2019-11-15
  • 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.

     

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  • [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)
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