基于转移和动态块分区直方图的多级可逆数据隐藏

发布时间：2023-07-27 08:18

　　本文研究了用于数据嵌入的预留空间。因为低压缩率不能提供足够的空间来控制额外的比特,而预留空间为数据嵌入提供额外的空间,所以预留空间在最大化数据嵌入中起关键作用。但小块分区上的结果不能容纳该块所需的足够数据,而在较低的块级别上,可以获得最高的嵌入容量,因此,基于动态分区的平滑区域和粗糙区域,对于以最大的嵌入率寻找到最佳的信息写入位置和保持高质量的图像具有重要作用。首先,本文提出了一种基于多比特平面高效压缩域的可逆数据隐藏新技术。本文通过一系列实验,利用分块方案对不同参数的结果进行了评价,并修正了直方图各分块的零点概率。该方案获得了更多的嵌入容量和高质量的隐写图像。实验结果有效地实现了高嵌入能力和保持图像质量的目标。其次,本文提出了一种创新的可逆数据隐藏技术,该技术利用多层局部化n位截断图像(LBPTI)在柱状图移动的基础上形成的,即通过有效无损压缩从8位平面生成。从块中选择参考点后,利用相邻的顶点,在不修改峰值点的情况下实现数据嵌入;另外,在提取端,提取秘文信息时,对峰值的关键信息并不是强制性提取的。为了使嵌入的覆盖图像与原始覆盖图像的直方图相似,在保持覆盖图像质量外,同时本文还利用低块...

【文章页数】：110 页

【学位级别】：博士

【文章目录】：
Acknowledgements
摘要
Abstract
1 Introduction
    1.1 Background and Scope
    1.2 Motivation
    1.3 Research methodology
    1.4 Dissertation Organization
2 A new Multilevel Reversible Bit-planes Data Hiding Technique Based on His-togram Shifting of Efficient Compressed Domain
    2.1 Related Work
        2.1.1 Ni et al.'s method
        2.1.2 Kim et al.'s method
        2.1.3 Che et al.'s method
    2.2 Proposed method
        2.2.1 Data embedding algorithm
        2.2.2 Data extraction and cover image retrieval algorithm
    2.3 Experiments
        2.3.1 Performance comparisons
        2.3.2 Block Divisions
        2.3.3 Computational Complexity
        2.3.4 Lower bound PSNR
    2.4 Conclusions
3 Efficient Lossless Compression based Reversible Data Hiding using Multilay-ered n-bit Localization
    3.1 Related work
        3.1.1 Kim et al.'s Scheme
        3.1.2 Che et al.'s Scheme
    3.2 Data embedding algorithm
        3.2.1 Embedding with n-bit localization
        3.2.2 Side information
        3.2.3 Data extraction algorithm
        3.2.4 Multilayer n-bit embedding
    3.3 Experiments
        3.3.1 N-bit plane with different embedding layer
        3.3.2 Comparison with existing algorithms
        3.3.3 Embedded capacity versus PSNR
    3.4 Conclusions
4 Generalized PVO base Reversible Data Hiding Using Firefly Algorithm
    4.1 Introduction
    4.2 Related work
    4.3 Proposed Schemes
        4.3.1 Quadtree image partition
        4.3.2 Embedding scheme
        4.3.3 Extraction scheme
        4.3.4 Data embedding and extraction procedures
    4.4 Experiments
        4.4.1 Analysis of Proposed Method
        4.4.2 Special Blocks Handling
        4.4.3 Performances Comparison
    4.5 Conclusions
5 RDH based histogram equalization using for Cancer prediction and recognitionfor Abnormal Tumor regions
    5.1 Related work
        5.1.1 Histogram smoothness by Gaussian filter
        5.1.2 Partition of new dynamic range
        5.1.3 Independently equalized each partition
        5.1.4 Normalization of image brightness
    5.2 Limited Dynamic weighted histogram equalization
        5.2.1 Image Preprocessing
        5.2.2 Data embedding and extraction procedures
    5.3 Experiments
        5.3.1 Analysis of Proposed Method
        5.3.2 Performance analysis for disease classification
    5.4 Conclusions
6 Conclusions and Future Directions
References
Publications



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