Reversible data hiding for color images based on prediction-error value ordering and adaptive embedding

Abstract

Prediction-error value ordering (PEVO) is an efficient implementation of reversible data hiding (RDH), which is perfect for color images to exploit the inter-channel and intra-channel correlations synchronously. However, the existing PEVO method has a slight shortage in the mapping selection stage, the candidate mappings are selected under conditions inconsistent with actual embedding in advance, and this is not the optimal solution. Therefore, in this paper, a novel RDH method for color images based on PEVO and adaptive embedding is proposed to implement adaptive two-dimensional (2D) modification for PEVO. Firstly, an improved particle swarm optimization (IPSO) algorithm based on PEVO is designed to alleviate the high temporal complexity caused by the determination of parameters and implement adaptive 2D modification for PEVO. Next, to further optimize the mapping used in embedding, an improved adaptive 2D mapping generation strategy is proposed by introducing the position information of points. In addition, a dynamic payload partition strategy is proposed to improve the embedding performance. Finally, the experimental results show that the PSNR of the image Lena is as high as 62.94 dB and the average PSNR of the proposed method is 1.46 dB higher than that of the state-of-the-art methods for embedding capacity of 20,000 bits.