High-fidelity reversible data hiding using dynamic prediction and expansion

Abstract. With the aim of reducing the distortion of the marked image to realize high-fidelity reversible data hiding (RDH), we propose an RDH scheme based on dynamic prediction and expansion (DPE). By introducing a dynamic pixels-value-ordering (D-PVO) method into DPE, a full reversibility can be achieved. The pixels of the cover image are predicted by sorting their cross-over neighbors and using the two end pixels or the median pixels to conduct the prediction. The covert data are embedded by using prediction-error-expansion. We have theoretically proved higher prediction accuracy for the proposed method compared with the two existing classical prediction methods. In our experiments, we first verify the prediction accuracy of D-PVO by applying it to Gaussian distributed sampled data. Next, we perform experiments on the standard pictures from USC-SIPI and Kodak image datasets. The experimental results demonstrate that the proposed scheme can ensure a full recovery of the original image and a higher fidelity of the marked cover images, and the peak signal-to-noise ratio can exceed 60.00 dB when embedding capacity reaches 10,000 bits.