• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus 收录
  • 全国中文核心期刊
  • 中国科技论文统计源期刊
  • 中国科学引文数据库来源期刊

提升高精度图像弱目标视觉质量的高效压缩算法

李英 李博 高新波

李英, 李博, 高新波. 提升高精度图像弱目标视觉质量的高效压缩算法[J]. 西南交通大学学报, 2019, 54(5): 1012-1020. doi: 10.3969/j.issn.0258-2724.20180180
引用本文: 李英, 李博, 高新波. 提升高精度图像弱目标视觉质量的高效压缩算法[J]. 西南交通大学学报, 2019, 54(5): 1012-1020. doi: 10.3969/j.issn.0258-2724.20180180
LI Ying, LI Bo, GAO Xinbo. Efficient Compression Algorithm for Improving Visual Quality ofWeak Targets in High Precision Images[J]. Journal of Southwest Jiaotong University, 2019, 54(5): 1012-1020. doi: 10.3969/j.issn.0258-2724.20180180
Citation: LI Ying, LI Bo, GAO Xinbo. Efficient Compression Algorithm for Improving Visual Quality ofWeak Targets in High Precision Images[J]. Journal of Southwest Jiaotong University, 2019, 54(5): 1012-1020. doi: 10.3969/j.issn.0258-2724.20180180

提升高精度图像弱目标视觉质量的高效压缩算法

doi: 10.3969/j.issn.0258-2724.20180180
基金项目: 国家杰出青年科学基金资助项目(61125204);青年科学基金资助项目(61201291)
详细信息
    作者简介:

    李英(1976—),女,高级工程师,博士研究生,研究方向为图像内容分析、编解码技术. 电话:13802881855,E-mail:liying_gd@139.com

  • 中图分类号: TN919.81

Efficient Compression Algorithm for Improving Visual Quality ofWeak Targets in High Precision Images

  • 摘要: 为了实现对高精度图像进行高效压缩,同时确保重建图像的弱目标区域有较好的保真效果,提出了一种提升弱目标区域质量的基于误差优化编码的高精度图像压缩算法. 首先,使用JPEG-LS (joint photographic experts group lossless)压缩算法对图像数据进行压缩,在游程编码过程中自适应地选择需要二次编码的误差数据,并完成了基于视觉质量的非均匀量化;其次,对量化值进行数据分解,去除量化值之间的相关性,并对分解后的数据进行MQ算术编码的熵编码;图像重建时根据量化间隔重建反量化值,并设计了反量化优化和滤波优化过程;最后,将本文算法与JPEG-LS、JPEG2000 (joint photographic experts group 2000)算法进行了性能比较,结果表明:本算法能够实现高精度图像数据的高效压缩,且复杂度低,易于硬件实现,虽然引入了误差数据二次优化编码等过程,但增加编码的数据量较小,故与JPEG-LS算法的压缩速度相当,然而比JPEG2000算法的压缩速度提升4.47倍;同时有效减少了常规算法造成的信息损失,重建图像的峰值信噪比与JPEG-LS、JPEG2000相当或略低,但弱目标区域的视觉质量及保真效果更好.

     

  • 图 1  MQ编码器

    Figure 1.  Schematic of MQ encoder

    图 2  编码算法流程

    Figure 2.  Coding algorithm flowchart

    图 3  解码算法流程

    Figure 3.  Decoding algorithm flowchart

    图 4  climit变化对r的影响

    Figure 4.  Effect of climit on the overall code rate

    图 5  与JPEG-LS算法的重建图像的PSNR比较

    Figure 5.  PSNR comparison between the proposed method and JPEG-LS algorithm

    图 6  与JPEG2000算法的重建图像的PSNR比较

    Figure 6.  Comparison of PSNR between the proposed method and JPEG2000 algorithm

    图 7  重建图像视觉质量对比(遥感图像1)

    Figure 7.  Comparison of reconstructed image visual quality (remote sensing image 1)

    图 8  重建图像视觉质量对比(遥感图像2)

    Figure 8.  Comparison of reconstructed image visual quality (remote sensing image 2)

    表  1  编码性能随cnear变化情况

    Table  1.   Coding performance variation with cnear

    cnearL1/BL2/BL1+L2/Br
    10 383 251 25 030 408 281 3.115
    12 355 741 29 728 385 469 2.941
    14 333 915 32 475 366 390 2.795
    16 315 313 33 107 348 420 2.658
    18 298 920 41 370 340 290 2.596
    20 284 974 44 285 329 259 2.512
    22 272 834 46 675 319 509 2.438
    24 262 021 48 573 310 594 2.369
    26 252 160 50 043 302 203 2.306
    28 243 237 50 725 293 962 2.243
    30 235 132 51 748 286 880 2.189
    下载: 导出CSV

    表  2  PSNR随cnear变化情况

    Table  2.   Variation of PSNR with cnear

    cnearJPEG-LS
    重建图像
    误差编码
    重建图像
    滤波后的
    图像
    10 56.06 57.65 56.94
    12 55.48 56.26 55.46
    14 54.22 54.82 54.22
    16 53.11 53.84 53.24
    18 52.16 52.87 52.27
    20 51.28 51.98 51.42
    22 50.51 51.21 50.65
    24 49.78 50.55 49.95
    26 49.12 49.78 49.31
    28 48.50 49.05 48.66
    30 47.95 48.42 48.12
    32 47.47 47.95 47.63
    34 47.14 47.75 47.32
    36 46.67 47.21 46.86
    38 46.25 46.89 46.48
    40 45.98 46.32 46.04
    下载: 导出CSV

    表  3  图像压缩时间比较

    Table  3.   Comparison of imagecompression time

    算法JPEG-LSJPEG2000本文算法
    压缩时间2.019.882.21
    下载: 导出CSV
  • DENG J X, DENG H T. An image joint compression-encryption algorithm based on adaptive arithmetic coding[J]. Chin. Phys. B, 2013, 22(9): 1-6.
    邓家先,任玉莉. 基于改进零树编码的图像联合压缩加密算法[J]. 光子学报,2013,42(1): 121-126.

    DENG Jiaxian, RENG Yuli. Image joint compression encryption algorithm based on improved zero tree coding[J]. Acta Photonica Sinica, 2013, 42(1): 121-126.
    ZHANG K, TAO D, GAO X, et al. Coarse-to-fine learning for single-image super-resolution[J]. IEEE Transactions on Neural Networks and Learning Systems (IEEE-TNNLS), 2017(28): 1109-1122.
    DJELOUAT H, ALI A, AMIRA A, et al. Compressive sensing based electronic nose platform[J]. Digital Signal Processing, 2016, 60: 350-359.
    DROST G W, BOURBAKIS N G. A hybrid system forreal-time lossless image compression[J]. Micropro-cessors and Microsystems, 2001, 25(1): 19-31. doi: 10.1016/S0141-9331(00)00102-2
    TU C J, TRAN T D. Contex-based entropy coding of block transform coefficients for image compression[J]. IEEE Transactions on Image Processing, 2002, 11(11): 127-128.
    SHI C, ZHANG J, ZHANG Y. Content-based onboard compression for remote sensing images[J]. Neurocomputing, 2016, 191: 330-340. doi: 10.1016/j.neucom.2016.01.048
    CHEN H Y, CHANG C C. A new lossless compresion scheme based on huffman coding scheme for image compression[J]. Signal Processing: Image Communi- cation, 2000, 16(4): 367-372. doi: 10.1016/S0923-5965(99)00064-8
    KONG F Q, WU X Y. An improved distributed source coding and ROI coding-based interferometric multi-spectral image compression alogrithm[J]. Journal of Astronautics, 2011, 32(2): 367-373.
    DENG C W, LIN W S, CAI J F. Content-based image compression for arbitrary- resolution display devices[J]. IEEE Trans. on Multimiedia, 2012, 14(4): 1127-1139. doi: 10.1109/TMM.2012.2191270
    BRUYLABTS T, MUNTEANU A, SCHELKENS P. Wavelet based volumetric medical image compre-ssion[J]. Signal Processing Image Communication, 2015, 31(36): 112-133.
    石翠萍,张钧萍,张晔. 基于自适应扫描的图像压缩方法[J]. 系统工程与电子技术,2016,38(1): 193-199. doi: 10.3969/j.issn.1001-506X.2016.01.30

    SHI Cuiping, ZHANG Junping, ZHANG Ye. Image compression method mased on adaptive scanning[J]. Journal of Systems Engineering and Electronics, 2016, 38(1): 193-199. doi: 10.3969/j.issn.1001-506X.2016.01.30
    孔繁锵. 结合HVS和相似性度量的图像质量评价测度[J]. 中国图象图形学报,2011,16(7): 1184-1191. doi: 10.11834/jig.20110702

    KONG Fanqiang. Image quality assessment based on HVS and similarity measure[J]. Journal of Image and Graphics, 2011, 16(7): 1184-1191. doi: 10.11834/jig.20110702
    张毅,雷杰,李云松. 一种新的基于先验数据表的JPEG-LS动态码率控制算法[J]. 电子与信息学报,2014,36(4): 823-827.

    ZHANG Yi, LEI Jie, LI Yunsong. A novel dynamic rate control algorithm for JPEG-LS based on empirical data table[J]. Journal of Electronics & Information Technology, 2014, 36(4): 823-827.
    MUNADI K, UROSAKI M K, NISHIKAWA K, et al. Evaluation of JPEG2000 error resilience over OFDM channel[J]. Ieice Technical Report Image Engineering, 2017, 102(315): 37-42.
  • 加载中
图(8) / 表(3)
计量
  • 文章访问数:  440
  • HTML全文浏览量:  250
  • PDF下载量:  16
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-03-14
  • 修回日期:  2018-10-11
  • 网络出版日期:  2018-11-13
  • 刊出日期:  2019-10-01

目录

    /

    返回文章
    返回