CRDH: Compatible Reversible Data Hiding With High Capacity and Generalization

In reversible data hiding (RDH) in the plaintext domain, the reversibility of the data and the image is the greatest strength but also comes with limitations, such as low embedding capacity and weak generalization ability. These limitations make it challenging for RDH to be applied in scenarios that require the concealment of high-capacity data. To address these issues, we propose a compatible reversible data hiding with high capacity and generalization (CRDH), which can perform a second embedding based on all existing RDH methods and the two extractions are independent of each other. The nearest-neighbor interpolation (NNI) algorithm and integer wavelet transform are initially designed to create additional redundancy room, diverging from existing RDH methods that typically exploit the inherent redundancy within the image itself. Following this, we derive a novel method to prevent pixel value overflow or underflow, which is employed to guide the data embedding process. In the experimental results on standard test images, the average maximum embedding capacity of the CRDH method reaches 4.41 bits per pixel (BPP), which is 1.98 times that of other methods. As the embedded data increases, the peak signal-to-noise ratio (PSNR) of CRDH’s stego-images becomes higher compared to other methods. Furthermore, CRDH exhibits a significantly superior generalization ability in terms of both capacity and quality compared to state-of-the-art RDH methods.