Reversible data hiding with pairwise PEE and 2D-PEH decomposition

Yu, Chunqiang; Zhang, Xianquan; Wang, Dewang; Tang, Zhenjun

doi:10.1016/j.sigpro.2022.108527

Cited by 20 publications

(14 citation statements)

References 42 publications

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“…Figure 9 illustrates the comparison results. It can be seen that the proposed method outperforms 41 Table 14 Average PSNR comparison on 1000 images (unit: dB).…”

Section: Imagementioning

confidence: 99%

“…To generate PEH with lower entropy, Ou et al 36 introduced a hybrid histogram generation method, in which prediction errors with small-magnitude are paired to construct a reduced 2D-PEH, and the other prediction-errors are used to generate a 1D-PEH. Based on these extensions, some methods [37][38][39][40][41] integrating predictor and these extensions are proposed. Ou et al 37 extended IPVO into a 2D form using prediction-error pair within a block for data embedding.…”

Section: Introductionmentioning

confidence: 99%

“…In Ref. 41, a global 2D-PEH is decomposed into several sub 2D-PEHs. Two 2D-mapping is selected to modify the sub 2D-PEHs.…”

Section: Introductionmentioning

confidence: 99%

“…The existing plaintext based RDH method can be classified into four kinds: lossless compression based methods, 8 – 11 difference expansion (DE) based methods, 12 , 13 histogram shifting (HS) based methods, 14 , 15 and prediction error expansion (PEE) based methods 16 – 41 Lossless compression-based methods losslessly compress a part of the cover images to vacate the embedding room. DE techniques calculate a difference value and an average value of every two neighboring pixels.…”

Section: Introductionmentioning

confidence: 99%

“…introduced a hybrid histogram generation method, in which prediction errors with small-magnitude are paired to construct a reduced 2D-PEH, and the other prediction-errors are used to generate a 1D-PEH. Based on these extensions, some methods 37 – 41 integrating predictor and these extensions are proposed. Ou et al 37 .…”

Section: Introductionmentioning

confidence: 99%

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Reversible data hiding with prediction error histogram decomposition and hybrid dimensional histogram modification

Zhang

et al. 2023

J. Electron. Imag.

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A reversible data hiding (RDH) method with prediction error histogram (PEH) decomposition and hybrid dimensional histogram modification is proposed. First, a global one-dimensional (1D) PEH is generated by pixel-based pixel value ordering predictor. The proposed method fully utilizes the pixels inside the range of the minimum and maximum of neighbors to decompose 1D-PEH into several sub 1D-PEHs. Then, each sub 1D-PEH is separated into two parts. The part with small-magnitude is used to construct a reduced two-dimensional (2D) PEH, whereas the rest is shifted for reversibility. Finally, data embedding is performed by modifying each reduced 2D-PEH to reduce distortion. Experimental results demonstrates that the proposed method outperforms some high-fidelity RDH methods.

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“…Figure 9 illustrates the comparison results. It can be seen that the proposed method outperforms 41 Table 14 Average PSNR comparison on 1000 images (unit: dB).…”

Section: Imagementioning

confidence: 99%