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Adaptive digital image watermarking based on predictive embedding and a dynamic fuzzy inference system model.

机译:基于预测嵌入和动态模糊推理系统模型的自适应数字图像水印。

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摘要

A novel image watermarking scheme is introduced that consists of an adaptive watermarking algorithm based on a model of the Human Visual System (HVS) and a Dynamic Fuzzy Inference System (DFIS). This scheme relies on the DFIS to extract the human eye sensitivity knowledge using the HVS model. The DFIS and the HVS combined are used to adjust and select the appropriate watermark length as well as the watermarking strength for each pixel in an image. The main goal of the algorithm is to provide a more robust and imperceptible watermark. The primary contribution of this work is to present a unique approach to perform image-adaptive watermarking by introducing a dynamic fuzzy logic technique. This logic relies on the statistical distributions of the HVS-generated data in order to accurately approximate the relationship found between all properties of the human perceptual system. In addition, we introduce a Predictive Watermark Embedding (PWE) algorithm that exploits the average power of the frequency-domain image coefficients as well as the aforementioned DFIS model in order to insure that the watermark insertion process is adaptively and accurately performed in lower-frequency components and significant DCT coefficients. (Abstract shortened by UMI.)
机译:提出了一种新颖的图像水印方案,该方案包括基于人类视觉系统(HVS)和动态模糊推理系统(DFIS)模型的自适应水印算法。该方案依靠DFIS使用HVS模型提取人眼敏感性知识。结合使用DFIS和HVS来调整和选择图像中每个像素的适当水印长度以及水印强度。该算法的主要目标是提供更健壮和不易察觉的水印。这项工作的主要贡献是通过引入动态模糊逻辑技术,提出了一种执行图像自适应水印的独特方法。该逻辑依赖于HVS生成的数据的统计分布,以便准确地近似人类感知系统的所有属性之间发现的关系。此外,我们介绍了一种预测性水印嵌入(PWE)算法,该算法利用频域图像系数的平均功率以及上述DFIS模型,以确保在较低频率下自适应且准确地执行水印插入过程分量和显着的DCT系数。 (摘要由UMI缩短。)

著录项

  • 作者

    Sakr, Nizar.;

  • 作者单位

    University of Ottawa (Canada).;

  • 授予单位 University of Ottawa (Canada).;
  • 学科 Engineering Electronics and Electrical.
  • 学位 M.A.Sc.
  • 年度 2006
  • 页码 87 p.
  • 总页数 87
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

  • 入库时间 2022-08-17 11:40:57

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