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After the prediction, expansion embedding combined with histogram shifting techniques proposed in [10] are applied to hide information bits reversibly.
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In further support of this prediction, expansions required supplementation of the reaction with Mg2+, creatine kinase and dNTPs.
The general scenarios predicting "Expansion" were the most frequent among ENM predictions and the best-supported models among demographic simulations.
After that, prediction error expansion (PEE) was proposed by Thodi and Rodriguez [4] as a generalized form of difference expansion.
Another type of improvement called prediction error expansion (PEE) has exceeded the DE-based methods.
For exploring the correlation of the neighbor pixels/samples adequately, in [21], a non-integer prediction error expansion embedding method was proposed.
One of the mainstreams of the reversible data hiding is utilizing a combination of histogram shifting and prediction error expansion (PEE + HS) with good predictors.
In the classical PE embedding method proposed by Thodi and Rodriguez, the predicted value is rounded to integer number for integer prediction-error expansion (IPE) embedding.
This work aims at reducing the embedding distortion of prediction-error expansion (PE -based reversible watermarking.
A fruitful research direction, proposed by Thodi and Rodriguez [14], is prediction-error expansion (PE) embedding technique.
In the proposed watermarking method, we utilize a reversible data-embedding technique called prediction-error expansion on integers to achieve reversibility.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com