Multimedia Tools and Applications

, Volume 76, Issue 3, pp 4227–4242 | Cite as

Adaptive compressed sensing for wireless image sensor networks

  • Junguo Zhang
  • Qiumin Xiang
  • Yaguang Yin
  • Chen Chen
  • Xin Luo
Article
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Abstract

Compressed sensing (CS) based image compression can achieve a very low sampling rate, which is ideal for wireless sensor networks with respect to their energy consumption and data transmission. In this paper, an adaptive compressed sensing rate assignment algorithm that is based on the standard deviations of image blocks is proposed. Specifically, each image block is first assigned a fixed sampling rate. In addition to the fixed sampling rate, an adaptive sampling rate is then given to each block based on the standard deviation of the block. With this adaptive sampling strategy, higher sampling rates are assigned to blocks that are less compressible (e.g., blocks with complex textures are less compressible than blocks with a smooth background). The sensing matrix is constructed based on the assigned sampling rate. The fixed measurements and the adaptive measurements are concatenated to form the final measurements. Finally, the measurements are used to reconstruct the image on the decoding side. The experimental results demonstrate that the proposed algorithm can achieve image progressive transmission and improve the reconstruction quality of the images.

Keywords

Wireless sensor networks Block compressed sensing Adaptive sampling Rate allocation 
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Notes

Acknowledgments

This work was funded by The National Natural Science Foundation of China (Grant No. 31300470), Import Project under China State Forestry Administration (Grant No.2014-4-05), Beijing Higher Education Young Elite Teacher Project (Grant No.YETP0760).

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Junguo Zhang
    • 1
  • Qiumin Xiang
    • 1
  • Yaguang Yin
    • 2
  • Chen Chen
    • 3
  • Xin Luo
    • 1
  1. 1.School of TechnologyBeijing Forestry UniversityBeijingChina
  2. 2.Academy of Broadcasting Science, SAPPRFTBeijingChina
  3. 3.Department of Electrical EngineeringUniversity of Texas at DallasRichardsonUSA

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