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基于双像素值排序和最小预测误差扩展的可逆数据隐藏。

Reversible data hiding with dual pixel-value-ordering and minimum prediction error expansion.

机构信息

Department of Electrical, Electronic and Communication Engineering (EECE), Military Institute of Science and Technology (MIST), Mirpur Cantonment, Dhaka, Bangladesh.

Department of EECE, Engineering Faculty, Bangladesh Military Academy (BMA), Chattogram, Bangladesh.

出版信息

PLoS One. 2022 Aug 16;17(8):e0271507. doi: 10.1371/journal.pone.0271507. eCollection 2022.

DOI:10.1371/journal.pone.0271507
PMID:35972923
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9380960/
Abstract

Pixel Value Ordering (PVO) holds an impressive property for high fidelity Reversible Data Hiding (RDH). In this paper, we introduce a dual PVO (dPVO) for Prediction Error Expansion (PEE), and thereby develop a new RDH scheme to offer a better rate-distortion performance. Particularly, we propose to embed in two phases: forward and backward. In the forward phase, PVO with classic PEE is applied to every non-overlapping image block of size 1 × 3. In the backward phase, minimum-set and maximum-set of pixels are determined from the pixels predicted in the forward phase. The minimum set only contains the lowest predicted pixels and the maximum set contains the largest predicted pixels of each image block. Proposed dPVO with PEE is then applied to both sets, so that the pixel values of minimum set are increased and that of the maximum set are decreased by a unit value. Thereby, the pixels predicted in the forward embedding can partially be restored to their original values resulting in both a better embedded image quality and a higher embedding rate. Experimental results have recorded a promising rate-distortion performance of our scheme with a significant improvement of embedded image quality at higher embedding rates compared to the popular and state-of-the-art PVO-based RDH schemes.

摘要

像素值排序(PVO)在高保真度可恢复数据隐藏(RDH)中具有令人印象深刻的属性。在本文中,我们为预测误差扩展(PEE)引入了双 PVO(dPVO),从而开发了一种新的 RDH 方案,以提供更好的率失真性能。特别是,我们建议分两个阶段进行嵌入:前向和后向。在前向阶段,使用经典 PEE 的 PVO 应用于大小为 1×3 的每个非重叠图像块。在后向阶段,从前向阶段预测的像素中确定最小集和最大集。最小集仅包含最低预测像素,最大集包含每个图像块的最大预测像素。然后将具有 PEE 的提议的 dPVO 应用于这两个集合,使得最小集合的像素值增加一个单位值,而最大集合的像素值减少一个单位值。这样,在前向嵌入中预测的像素可以部分恢复到其原始值,从而在更高的嵌入率下提高嵌入图像质量和更高的嵌入率。实验结果记录了我们的方案具有有希望的率失真性能,与流行的和最先进的基于 PVO 的 RDH 方案相比,在更高的嵌入率下,嵌入图像质量有了显著提高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4790/9380960/a8b911076bbc/pone.0271507.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4790/9380960/5d653ff1631b/pone.0271507.g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4790/9380960/9629e004f75c/pone.0271507.g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4790/9380960/c84b255e44b2/pone.0271507.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4790/9380960/7f34fb6bb4f7/pone.0271507.g007.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4790/9380960/5d653ff1631b/pone.0271507.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4790/9380960/6beb450b7a08/pone.0271507.g002.jpg
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本文引用的文献

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2
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PLoS One. 2019 Mar 6;14(3):e0212093. doi: 10.1371/journal.pone.0212093. eCollection 2019.
3
Reversible integer wavelet transform for blind image hiding method.用于盲图像隐藏方法的可逆整数小波变换
PLoS One. 2017 May 12;12(5):e0176979. doi: 10.1371/journal.pone.0176979. eCollection 2017.
4
Adaptive Pairing Reversible Watermarking.自适应配对可逆水印。
IEEE Trans Image Process. 2016 May;25(5):2420-22. doi: 10.1109/TIP.2016.2549458.
5
Rate and Distortion Optimization for Reversible Data Hiding Using Multiple Histogram Shifting.基于多次直方图平移的可逆数据隐藏的速率失真优化。
IEEE Trans Cybern. 2017 Feb;47(2):315-326. doi: 10.1109/TCYB.2015.2514110. Epub 2016 Jan 27.
6
High Capacity Reversible Data Hiding in Encrypted Images by Patch-Level Sparse Representation.基于补丁级稀疏表示的加密图像大容量可逆数据隐藏。
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7
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IEEE Trans Image Process. 2014 Apr;23(4):1779-90. doi: 10.1109/TIP.2014.2307482.
8
Pairwise prediction-error expansion for efficient reversible data hiding.基于预测误差的成对扩展实现高效的可逆数据隐藏。
IEEE Trans Image Process. 2013 Dec;22(12):5010-21. doi: 10.1109/TIP.2013.2281422.
9
A novel joint data-hiding and compression scheme based on SMVQ and image inpainting.一种基于 SMVQ 和图像修复的新联合数据隐藏和压缩方案。
IEEE Trans Image Process. 2014 Mar;23(3):969-78. doi: 10.1109/TIP.2013.2260760. Epub 2013 Apr 30.
10
Efficient reversible watermarking based on adaptive prediction-error expansion and pixel selection.基于自适应预测误差扩展和像素选择的高效可逆水印。
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