留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

面向量化分块压缩感知的区域层次化预测编码

刘浩 郑浩然 黄荣

刘浩, 郑浩然, 黄荣等 . 面向量化分块压缩感知的区域层次化预测编码[J]. 北京航空航天大学学报, 2022, 48(8): 1376-1382. doi: 10.13700/j.bh.1001-5965.2021.0511
引用本文: 刘浩, 郑浩然, 黄荣等 . 面向量化分块压缩感知的区域层次化预测编码[J]. 北京航空航天大学学报, 2022, 48(8): 1376-1382. doi: 10.13700/j.bh.1001-5965.2021.0511
LIU Hao, ZHENG Haoran, HUANG Ronget al. Region-hierarchical predictive coding for quantized block compressive sensing[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1376-1382. doi: 10.13700/j.bh.1001-5965.2021.0511(in Chinese)
Citation: LIU Hao, ZHENG Haoran, HUANG Ronget al. Region-hierarchical predictive coding for quantized block compressive sensing[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1376-1382. doi: 10.13700/j.bh.1001-5965.2021.0511(in Chinese)

面向量化分块压缩感知的区域层次化预测编码

doi: 10.13700/j.bh.1001-5965.2021.0511
基金项目: 

国家自然科学基金 62001099

中央高校基本科研业务费专项资金 2232021G-09

详细信息
    通讯作者:

    刘浩, E-mail: liuhao@dhu.edu.cn

  • 中图分类号: TN919.8

Region-hierarchical predictive coding for quantized block compressive sensing

Funds: 

National Natural Science Foundation of China 62001099

the Fundamental Research Funds for the Central Universities 2232021G-09

More Information
  • 摘要:

    在量化分块压缩感知的预测编码中,低参考价值的候选者将导致较差的率失真性能。为了高效地降低编码失真,提出了一种基于螺旋逐块扫描的区域层次化预测编码方法。在以同一采样率进行观测后,各块按由内向外的扫描次序进行预测与量化。当前观测矢量从上下文感知候选集中选取与之具有最小误差的反量化矢量,作为其预测矢量;根据层次相关性,所有块被划分到3种区域之一,通过块编码模型为不同区域设定自适应的质量因子,关键区域被赋予较大的质量因子。与现有的预测编码方法相比,所提方法综合利用了矢量之间的空域相关性和层次相关性,实验结果获得了至少0.12 dB的率失真增益。

     

  • 图 1  QBCS测量端的功能模块及矢量

    Figure 1.  Modules and vectors of QBCS measurement end

    图 2  上下文感知候选集的示意图

    Figure 2.  Schematic diagram of context-aware candidate set

    图 3  逐块预测过程中的层次相关性

    Figure 3.  Hierarchical correlation during block-by-block measurement prediction

    图 4  三种区域的示例

    Figure 4.  Diagram of three regions

    图 5  实验中使用的测试图像

    Figure 5.  Test images used in the experiments

    图 6  经验值τ对RHPC方法的性能影响

    Figure 6.  Influence of empirical value τ on RHPC performance

    图 7  基于RHPC方法的Lena重构图像(使用典型的(S, Q)组合)

    Figure 7.  Reconstructed images of Lena by RHPC with typical (S, Q) combinations

    图 8  不同预测编码方法的平均率失真曲线

    Figure 8.  Average rate-distortion curves of different predictive coding methods

  • [1] CHEN Z, HOU X S, SHAO L, et al. Compressive sensing multi-layer residual coefficients for image coding[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(4): 1109-1120. doi: 10.1109/TCSVT.2019.2898908
    [2] PEETAKUL J, ZHOU J J, WADA K. A measurement coding system for block-based compressive sensing images by using pixel-domain features[C]//2019 Data Compression Conference (DCC). Piscataway: IEEE Press, 2019: 599.
    [3] UNDE A S, DEEPTHI P P. Rate-distortion analysis of structured sensing matrices for block compressive sensing of images[J]. Signal Processing: Image Communication, 2018, 65: 115-127. doi: 10.1016/j.image.2018.03.019
    [4] FOWLER J E, MUN S, TRAMEL E W. Block-based compressed sensing of images and video[J]. Foundations and Trends in Signal Processing, 2012, 4(4): 297-416.
    [5] WANG L J, WU X L, SHI G M. Binned progressive quantization for compressive sensing[J]. IEEE Transactions on Image Processing, 2012, 21(6): 2980-2990. doi: 10.1109/TIP.2012.2188810
    [6] PUDI V, CHATTOPADHYAY A, LAM K Y. Efficient and lightweight quantized compressive sensing using μ-law[C]//2018 IEEE International Symposium on Circuits and Systems. Piscataway: IEEE Press, 2018: 1-5.
    [7] RAPP J, DAWSON R M A, GOYAL V K. Estimation from quantized Gaussian measurements: When and how to use dither[J]. IEEE Transactions on Signal Processing, 2019, 67(13): 3424-3438. doi: 10.1109/TSP.2019.2916046
    [8] WANG X Q, LI G, QUAN C, et al. Distributed detection of sparse stochastic signals with quantized measurements: The generalized Gaussian case[J]. IEEE Transactions on Signal Processing, 2019, 67(18): 4886-4898. doi: 10.1109/TSP.2019.2932884
    [9] TRAN T T T, PEETAKUL J, PHAM C D K, et al. Bi-directional intra prediction based measurement coding for compressive sensing images[C]//2020 IEEE 22nd International Workshop on Multimedia Signal Processing. Piscataway: IEEE Press, 2020: 1-6.
    [10] SHI W, JIANG F, LIU S, et al. Image compressed sensing using convolutional neural network[J]. IEEE Transactions on Image Processing, 2019, 29: 375-388.
    [11] MUN S, FOWLER J E. DPCM for quantized block-based compressed sensing of images[C]//2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO). Piscataway: IEEE Press, 2012: 1424-1428.
    [12] ZHANG J, ZHAO D B, JIANG F. Spatially directional predictive coding for block-based compressive sensing of natural images[C]//2013 IEEE International Conference on Image Processing. Piscataway: IEEE Press, 2013: 1021-1025.
    [13] LI R, LIU H B, HE W, et al. Space-time quantization and motion-aligned reconstruction for block-based compressive video sensing[J]. KSⅡ Transactions on Internet and Information Systems, 2016, 10(1): 321-340.
    [14] TIAN W, LIU H. Measurement-domain spiral predictive coding for block-based image compressive sensing[C]//Proceedings of 10th International Conference on Image and Graphics. Piscataway: IEEE Press, 2019: 3-12.
    [15] CHEN Q L, CHEN D R, GONG J L. Weighted predictive coding methods for block-based compressive sensing of images[C]//2020 3rd International Conference on Unmanned Systems (ICUS). Piscataway: IEEE Press, 2020: 587-591.
    [16] YUAN X, HAIMI-COHEN R. Image compression based on compressive sensing: End-to-end comparison with JPEG[J]. IEEE Transactions on Multimedia, 2020, 22(11): 2889-2904. doi: 10.1109/TMM.2020.2967646
    [17] ZHANG Z, FANG R, LIN J, et al. A novel rate control method for still image coding[C]//Proceedings of 5th International Conference on Computer and Communications. Piscataway: IEEE Press, 2019: 1777-1781.
    [18] TREVISI M, AKBARI A, TROCAN M, et al. Compressive imaging using RIP-compliant CMOS imager architecture and landweber reconstruction[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(2): 387-399. doi: 10.1109/TCSVT.2019.2892178
  • 加载中
图(8)
计量
  • 文章访问数:  60
  • HTML全文浏览量:  8
  • PDF下载量:  18
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-09-03
  • 录用日期:  2021-09-17
  • 刊出日期:  2021-09-28

目录

    /

    返回文章
    返回
    常见问答