期刊文献+

基于数学形态学的二值文字水印信息稀疏表征方法 被引量:2

The Sparse Method of Binary Text Watermark Information Based on Mathematical Morphology
下载PDF
导出
摘要 水印信息的最佳稀疏域是基于压缩感知理论的矢量数据水印算法研究的基础,也是解决小数据量矢量数据水印嵌入的关键所在.本文提出了一种基于数学形态学的二值文字水印信息稀疏表征方法,分析了二值文字水印信息的特征,提出了基于数学形态学的水印信息稀疏表征方法,将原始的水印信息有效地进行了稀疏表达,并对提出的稀疏表达方法进行了实验验证.结果表明,该方法能够较好地对二值文字水印信息进行稀疏表达,有效提高了水印信息的压缩比,可以去除无关水印判读的冗余信息,为满足基于压缩感知理论水印算法的研究提供了好的理论基础. The optimized sparse domain of watermarking information is the foundation of studying watermarking algo- rithm for vector geographic data based on compression sensing theory, and it is also the key to solve the challenge to em- bed the watermarks into small data. A sparse representation based on mathematical ecology is proposed for binary-char- acter watermarking information. Firstly, the features of the binary-character watermarking information are analyzed. Then, the spare representation based on mathematical ecology is proposed, and it is used for the original watermarking information. Finally, the experimental verification is given for the proposed sparse representation. The results show that the method can embed the binary-character watermarking information in a sparse way, increase the compression ratio for watermarking information effectively, and remove the redundant information unrelated with identification of watermark- ing. Therefore, the proposed method provides a good theoretical foundation for research of watermarking algorithm based on compression sensing theory.
出处 《南京师范大学学报(工程技术版)》 CAS 2015年第3期40-44,共5页 Journal of Nanjing Normal University(Engineering and Technology Edition)
基金 国家自然科学基金(41301413) 江苏省自然科学基金(BK20130903)
关键词 数学形态学 二值水印 压缩感知 稀疏 mathematical morphology, binary watermark, compression sensing, sparse
  • 相关文献

参考文献9

  • 1Donoho L D. Compressed sensing[j]. IEEE Transactions on Information Theory ,2006,52(4): 1 289-1 306.
  • 2Candes E. Compressive sampling [C]//Proceedings of the International Congress of Mathematicians. Madrid, Spain, 20061 433 - 1 452.
  • 3Candes E,Romberg J,Tao T. Robust uncertainty principles : exact signal reconstruction from highly incomplete frequency information [j ]. IEEE Transactions on Information Theory,2006,52(2): 489-509.
  • 4Kumar A A, Makur A. Lossy compression of encrypted image by compressive sensing technique [C]//2009 IEEE Region 10Conference. Singapore,2009:1一5.
  • 5Zhang X, Ren Y,Feng G,et al. Compressing encrypted image using compressive sensing[C]//International Conference on In-telligent Information Hiding and Multimedia Signal Processing. Dalian ,2011 : 222-225.
  • 6Xu T’Zhen Y,Shao X. Novel speech secure communication system based on information hiding and compressed sensing[C]//International Conference on Systems and Networks Communications. Porto, Portugal, 2009 : 201-206.
  • 7Valenzise G,Tagliasacchi M,Tubaro S,et al. A compressive-sensing based watermarking scheme for sparse image tamperingidentification[ C ]//IEEE International Conference on Image Processing. Cairo, Egypt, 2009 : 1 265-1 268.
  • 8赵春晖,刘巍.基于分块压缩感知的图像半脆弱零水印算法[J].自动化学报,2012,38(4):609-617. 被引量:36
  • 9周燕,曾凡智.基于压缩传感的视频水印算法[J].计算机应用,2011,31(6):1508-1511. 被引量:1

二级参考文献17

共引文献35

同被引文献14

引证文献2

二级引证文献5

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部