A reversible information hiding method based on self-adaptive expansion of a two-dimensional prediction error histogram
A prediction error and histogram technology, applied in the field of information security, can solve problems such as the inability to reflect the advantages of high-dimensional histograms and optimal mapping strategies, and the lack of flexibility in algorithm design, so as to improve the embedding performance and ensure the embedding capacity.
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[0047] For different natural images, their different degrees of smoothness will lead to different characteristics of histograms, and the number of prediction error pairs and the corresponding modification will greatly affect the capacity and distortion brought by the embedding. In the traditional prediction error expansion (Prediction-Error Expansion, PEE), usually, the prediction error histogram will obey the Laplace-like distribution centered on 0 or close to 0, then the histogram 0 and -1 There is a high probability that the area where the pixels are located is large enough and smooth enough to be used for embedding. Therefore, we select all pixels with prediction error values of 0 and -1 to embed, and the other points with larger prediction error values are not embedded, but only shifted, without increasing the capacity. However, if the peaks of the histogram are concentrated between 0 and 1, then the optimal embedding positions should be 0 and 1, and the number of emb...
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