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Recently, a method based on multiple histograms modification (MHM) is proposed for reversible data hiding (RDH), in which a sequence of prediction-error histograms are generated and two expansion bins are selected in each histogram for expansion embedding. However, although efficient, it only chooses a single pair of expansion bins which limits the embedding capacity. On the other hand, the exhaustive expansion-bin-selection procedure in MHM takes huge computation time, so that it cannot be extended for high capacity RDH. In order to overcome the aforementioned drawbacks, an optimal RDH scheme based on MHM for high capacity embedding is proposed in this paper. First, to improve the embedding capacity, instead of a single pair of expansion bins, multiple pairs of expansion bins are utilized for each histogram, and the multiple-expansion-bin-selection for optimal embedding is formulated as an optimization problem. Then, unlike the exhaustive searching way used in MHM, a computationally efficient algorithm is proposed to solve the optimization problem, so that the optimal expansion bins can be adaptively determined to optimize the embedding performance. By the proposed approach, high embedding capacity can be achieved with good marked image quality, and the experimental results show that it is better than the original MHM and some other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Pixel-value-ordering (PVO) is an efficient technique of reversible data hiding (RDH). By PVO, the maximum and minimum in each cover image block are first predicted and then modified to embed data. Actually, many PVO-based methods are essentially based on high-dimensional histogram modification. For these methods, a two-dimensional (2D) prediction-error histogram (PEH) is first generated and then modified based on a 2D mapping. However, these methods have two drawbacks. On one hand, the generated 2D PEH is irregular so that it is difficult to design suitable histogram modification strategy. On the other hand, the employed 2D mapping is empirically designed, and thus the embedding performance is far from optimal. Based on these considerations, a new PVO-based RDH scheme is proposed in this paper. By considering both pixel value orders and pixel locations, a new predictor is proposed so that the generated 2D PEH is regular in shape and suitable for reversible embedding. Moreover, instead of manually designing 2D mappings, to optimize the embedding performance, a self-learning mechanism is proposed to adaptively select the 2D mapping according to the image content. With the new predictor and the self-learning mechanism for 2D mapping selection, the proposed method works well with a good marked image quality, e.g., the PSNR of the image Lena is as high as 61.53 dB for an embedding capacity of 10 000 bits. Besides, compared with some state-of-the-art RDH methods, the superiority of the proposed method is experimentally verified. [ABSTRACT FROM AUTHOR]

• A novel high-fidelity reversible embedding scheme using prediction-error value ordering and multiple embedding is proposed. • A prediction-error is computed by considering the difference between the top two maximum (minimum) prediction-errorswithin each image block. • A multiple-embedding mechanism is proposed to adaptive determine the number of modified prediction-errors. Prediction-error expansion (PEE) is the most widely investigated reversible data hiding (RDH) framework. However, the performance of PEE can be further improved since the image redundancy has not fully exploited yet. To enlarge the embedding capacity and optimize the embedding performance of RDH, in this paper, a novel high-fidelity reversible embedding scheme using prediction-error value ordering and multiple embedding is proposed. Incorporating with PEE, the inter-correlation of prediction-errors is exploited by ordering the prediction-errors values, and multiple to-be-modified prediction-errors within each image block are adaptive determined. Specifically, firstly, a newly defined prediction-error is computed by considering the difference between the top two maximum (minimum) prediction-errors within each image block. Then, these prediction-errors are modified for expansion embedding. Moreover, a multiple-embedding mechanism is introduced to adaptive determine the number of modified prediction-errors. Finally, extensive experiments are conducted, and it is experimentally verified that the proposed method is superior to than some state-of-the-art RDH works. A benchmark result is that, for the standard Lena image, a PSNR as high as 62.01 dB can be derived for an embedding capacity of 10,000 bits. [ABSTRACT FROM AUTHOR]