PVO-Based Reversible Data Hiding Using Global Sorting and Fixed 2D Mapping Modification

Wu, Yuhong; Hu, Runwen; Xiang, Shijun

doi:10.1109/tcsvt.2023.3282649

Cited by 4 publications

(12 citation statements)

References 44 publications

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“…Baboon is ignored in the 20,000bits comparison because the comparison method cannot realize the embedding capacity of 20,000 bits. When the payload is 10,000 bits, the average PSNR values of recursion-PVO are higher than [24], [26], [28] and [29] by 3.58 dB, 2.56 dB, 2.61 dB, 2.4 dB, respectively.When the payload is 20,000 bits, the average PSNR values of recursion-PVO are higher than [24], [26], [28] and [29] by 5.12 dB, 3.35 dB, 3.59 dB, and 3.36 dB.…”

Section: Fidelitymentioning

confidence: 91%

“…In addition, four state-of-the-art RDH methods, including [24], [26], [28] and [29] are used for comparison. The comparison results are shown in Fig.…”

Section: Fidelitymentioning

confidence: 99%

“…Fig. 7 Performance comparison for the proposed method and some state-of-the-art works including [24], [26], [28] and [29]…”

Section: Author Contributionsmentioning

confidence: 99%

“…In the above methods, the innovation of improvement is focused mainly on the selection of blocks in order to realize the performance improvement, but no matter how the improvement is made, at most only two bits of data are embedded into a block. Therefore, Ou et al [24] proposed to apply the prediction error histogram to the pixel value ordering algorithm by using the PVO algorithm as a predictor to get the prediction error, and then embed the secret data using the measurement error histogram shifting algorithm, which was later widely used [27,28].…”

Section: Introductionmentioning

confidence: 99%

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Reversible Data Hiding for Recursion-PVO Based on Pixels Selection

Ge,

Shen,

Xia

et al. 2024

Preprint

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Pixel value ordering (PVO) is one of the most commonly used methods in reversible data hiding (RDH). Most PVO methods currently chunk images, categorize them according to the space complexity of the chunks, and adopt different embedding strategies for different chunk categories. Although this approach achieves some results, the utilization of pixel redundancy is still insufficient. In order to expand the embedding capacity of RDH and improve the embedding performance, a high-fidelity reversible data hiding method based on pixels selection and recursion-PVO is proposed in this paper. Specially, for uniformly divided non-overlapping blocks, the set of pixels with the smallest and second-smallest variance values within each block is selected to form a new block to be embedded, thus further utilizing pixel redundancy. Then, the embedding capacity is improved by transforming some reference pixels into to-be-embedded pixels and forming pixel pairs with other reference pixels to calculate the prediction error. Experimental results show that the proposed method has better performance compared to some state-of-the-art methods. In particular, for the commonly used test image Lena, the PSNR after embedding 10,000 bits of data is up to 63.13 dB.

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Section: Fidelitymentioning

confidence: 91%

“…In addition, four state-of-the-art RDH methods, including [24], [26], [28] and [29] are used for comparison. The comparison results are shown in Fig.…”

Section: Fidelitymentioning

confidence: 99%

“…Fig. 7 Performance comparison for the proposed method and some state-of-the-art works including [24], [26], [28] and [29]…”

Section: Author Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reversible Data Hiding for Recursion-PVO Based on Pixels Selection

Ge,

Shen,

Xia

et al. 2024

Preprint

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“…High embedding capacity and security are the outcomes of this. Wu et al [ 21 ] proposed a global sorting strategy combining local and global image features for reversible data hiding. For each pixel, its predicted value and local complexity are first calculated based on its local features.…”

Section: Introductionmentioning

confidence: 99%

An n-Dimensional Chaotic Map with Application in Reversible Data Hiding for Medical Images

Yang,

Chang,

Feng

et al. 2024

Entropy

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The drawbacks of a one-dimensional chaotic map are its straightforward structure, abrupt intervals, and ease of signal prediction. Richer performance and a more complicated structure are required for multidimensional chaotic mapping. To address the shortcomings of current chaotic systems, an n-dimensional cosine-transform-based chaotic system (nD-CTBCS) with a chaotic coupling model is suggested in this study. To create chaotic maps of any desired dimension, nD-CTBCS can take advantage of already-existing 1D chaotic maps as seed chaotic maps. Three two-dimensional chaotic maps are provided as examples to illustrate the impact. The findings of the evaluation and experiments demonstrate that the newly created chaotic maps function better, have broader chaotic intervals, and display hyperchaotic behavior. To further demonstrate the practicability of nD-CTBCS, a reversible data hiding scheme is proposed for the secure communication of medical images. The experimental results show that the proposed method has higher security than the existing methods.

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