Optics and Precision Engineering, Volume. 32, Issue 20, 3112(2024)

Image compression algorithm for fuze radome deformation images

Haoyang ZHANG1... Fan WANG1,*, Senmu SHAO2, Hui TIAN1 and Rui CHEN1 |Show fewer author(s)
Author Affiliations
  • 1School of Opto-electronical Engineering, Xi’an Technological University, Xi’an7002, China
  • 2Xi’an Institute of Electromechanical Information Technology, Xi’an710065, china
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    Figures & Tables(20)
    Block diagram of the test system
    Interior image of fuze radome
    Overall flowchart of image compression algorithm in this paper
    Structure of 9/7M 2D wavelet filter
    LL4 subband weighted 2 image
    Comparison of decompressed images with 1-weighted and 1.2-weighted
    Comparison of compression magnification 13.89 times high frequency wavelet coefficients subband weights 1 and 0 decompression reconstructed image
    Image of SOT structure of SPIHT
    Encoding process diagram
    SPIHT coding and decoding result diagram
    Modified RLE encoding process diagram
    Modified RLE encoding result map
    Comparison of the effect of different compression algorithms
    • Table 1. Energy distribution of subbands at different wavelet decomposition levels

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      Table 1. Energy distribution of subbands at different wavelet decomposition levels

      小波子带LL4HL4LH4HH4HL3LH3HH3HL2LH2HH2HL1LH1HH1
      Energy21 8134 7994 7161 0663 2953 3044292 2792 293167000
    • Table 2. Signal-to-noise ratio of decompressed images with wavelet coefficients subband weights 0 and 1 at different compression multiplicities

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      Table 2. Signal-to-noise ratio of decompressed images with wavelet coefficients subband weights 0 and 1 at different compression multiplicities

      压缩倍率12.6513.8915.3817.8519.23
      SNR(权重0)36.9635.8834.4532.9630.56
      SNR(权重1)33.4532.2130.9729.4827.08
    • Table 3. Table of subbands weighting enhancement for wavelet transform

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      Table 3. Table of subbands weighting enhancement for wavelet transform

      子带HL1LH1HH1剩余子带
      权重0001
    • Table 4. Table of spatial orientation tree node

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      Table 4. Table of spatial orientation tree node

      名称分类标准
      Oi,j

      根节点所有Children节点的坐标集,

      Oi,j={(2i,2j),(2i,2j+1),(2i+1,2j),

      2i+1,2j+1)};

      Di,j

      根节点所有后代节点的坐标集,

      包括ChildrenGrandchildrenGreat-Grandchildren节点;

      Hi,j

      所有Parent根节点的坐标集合,

      H(i,j)=LL4LH4HL4HH4};

      Li,j

      根节点所有间接后代节点,

      Li,j=Di,j-Oi,j

    • Table 5. Comparison of compression performance of this paper’s algorithm, SPIHT, CCSDS, and JPEG2000

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      Table 5. Comparison of compression performance of this paper’s algorithm, SPIHT, CCSDS, and JPEG2000

      图像

      类型

      压缩

      倍率

      本文算法SPIHTCCSDSJPEG2000
      SSIMPSNR/dBSSIMPSNR/dBSSIMPSNR/dBSSIMPSNR/dB

      基准

      圆环

      图像

      3.030.996 546.121 320.988 841.235 230.971 438.835 840.997 1①46.424 11
      4.010.995 644.336 00.976 539.564 80.956 738.062 50.994 843.546 6
      5.280.994 243.478 50.958 238.374 30.936 437.687 20.992 342.896 2
      6.220.991 742.801 90.932 837.224 90.911 334.948 10.990 741.656 9
      7.200.990 441.624 70.923 436.568 10.903 934.173 50.989 441.075 9
      8.470.983 440.922 320.919 735.141 430.885 033.389 640.985 7①40.949 71
      9.410.975 739.475 50.893 033.975 90.864 732.497 90.974 839.352 0

      形变

      圆环

      图像

      3.030.996 145.971 420.984 440.235 230.977 238.586 840.996 8①45.980 51
      4.010.995 544.847 90.974 339.524 00.953 938.038 20.994 743.928 6
      5.280.994 143.009 50.947 637.848 80.933 537.356 10.992 841.895 9
      6.220.992 541.868 70.934 337.390 80.912 934.565 70.990 140.428 6
      7.200.990 640.374 30.913 234.721 80.908 434.420 40.988 439.834 5
      8.470.987 639.349 00.905 134.037 60.887 933.257 40.984 839.239 6
      9.410.977 038.368 220.885 832.891 830.864 832.415 540.980 4①38.919 41
    • Table 6. Comparison of compression times for different compression algorithms at a compression ratio of 8.47x

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      Table 6. Comparison of compression times for different compression algorithms at a compression ratio of 8.47x

      压缩算法基准圆环图像形变圆环图像
      SSIMPSNR/dB压缩时间/sSSIMPSNR/dB压缩时间/s
      本文算法0.983 440.922 33.888 60.987 639.349 03.883 4
      SPIHT0.919 735.141 43.829 60.905 134.037 63.358 4
      CCSDS0.885 033.389 62.912 70.887 933.257 42.936 7
      JPEG20000.985 740.949 76.015 40.984 839.239 65.997 8
    • Table 7. Comparison of parameterless sharpness for different compression algorithms at a compression ratio of 8.47x

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      Table 7. Comparison of parameterless sharpness for different compression algorithms at a compression ratio of 8.47x

      压缩算法基准圆环图像形变圆环图像
      本文算法45.84445.705
      SPIHT36.54236.495
      CCSDS30.37530.242
      JPEG200043.77843.736
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    Haoyang ZHANG, Fan WANG, Senmu SHAO, Hui TIAN, Rui CHEN. Image compression algorithm for fuze radome deformation images[J]. Optics and Precision Engineering, 2024, 32(20): 3112

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    Paper Information

    Category:

    Received: May. 21, 2024

    Accepted: --

    Published Online: Jan. 10, 2025

    The Author Email: WANG Fan (wangfan@xatu.edu.cn)

    DOI:10.37188/OPE.20243220.3112

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