• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Digital Signal Processing >Hiding data in compressive sensed measurements: A conditionally reversible data hiding scheme for compressively sensed measurements
【24h】

Hiding data in compressive sensed measurements: A conditionally reversible data hiding scheme for compressively sensed measurements

机译：在压缩感知测量中隐藏数据：用于压缩感知测量的条件可逆数据隐藏方案

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Most of the real-world signals we encounter in real-life applications have low information content In other words, these signals can be well approximated by sparse signals in a proper basis. Compressive sensing framework uses this fact and attempts to represent signals by using far fewer measurements as compared to conventional acquisition systems. While the CS acquisition is linear, the reconstruction of the signal from its sparse samples is nonlinear and complex. The sparse nature of the signal allows enough room for some additional data sequence to be inserted and exactly recovered along with the reconstructed signal. In this study, we propose to linearly embed and hide data in compressively sensed signals and nonlinearly reconstruct both of them using a deflationary approach. We investigate the embedding capacity as a function of signal sparsity and signal compression, as well as the noise sensitivity of the proposed algorithm. (C) 2015 Elsevier Inc. All rights reserved.
机译：我们在现实应用中遇到的大多数现实信号的信息含量都很低。换句话说，可以在适当的基础上通过稀疏信号很好地近似这些信号。压缩感测框架利用了这一事实，并试图通过使用比常规采集系统少得多的测量来表示信号。虽然CS采集是线性的，但从其稀疏样本中重构信号是非线性且复杂的。信号的稀疏性质为插入一些其他数据序列提供了足够的空间，并与重建的信号一起准确恢复。在这项研究中，我们建议将线性数据嵌入和隐藏在压缩感测信号中，并使用通气方法非线性地重建它们。我们研究了嵌入能力与信号稀疏性和信号压缩的关系，以及所提出算法的噪声敏感性。 （C）2015 Elsevier Inc.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号