Improved Rhombus Predictor for Reversible Data Hiding Based on PEHS and Sorting Using Linear Weight Fitting
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Abstract
The equal weighting values of a predictor for every image and embedding size cause mistakes. This article presents an improvement rhombus predictor for Reversible Data Hiding (RDH) based on prediction-error histogram shift (PEHS) and sorting. The rhombus predictor has been exploited significantly in many researches related to RDH. Four neighboring statics are used for prediction in order to obtain a small prediction-error value. When embed data into these values, there is low distortion. In this work, the researcher improved the traditional rhombus predictor by using linear weight fitting (LWF). Finding a suitable new weight value achieved maximum possible PSNR results in each image and each embedding size. The experimental results of the proposed method showed improvement the PSNR value compared to the rhombus predictor in previous work.
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