Research On Reversible Data Hiding Based On Entropy Sorting And Structural Similarity Constraint

Reversible data hiding(RDH),as being a typical asymmetric technology,has received a lot of attention in the past few years.The basic idea of RDH is to use characteristics of human sensory perceptual redundancy.It is not only concerned about the embedding data,but also pays attention to the carriers themselves.RDH ensures that the cover data and the embedded message can be extracted from the marked content precisely and losslessly.This important data hiding technique provides valuable functions in many fields,such as medical image protection,authentication and tamper carrier recovery,digital media copyright protection,military imagery and legal,where the cover can not be damaged during data extraction.Therefore,how to propose efficient reversible information hiding algorithm has an important significance on the security of visual multimedia.Efficient reversible information hiding algorithms need to address three key issues.Firstly,how to construct a sharply distributed prediction error histogram?Secondly,how to optimize the embedding order?Thirdly,how to design encoding method based on visual evaluation criteria?The main contributions of this dissertation are as follows:1.Proposed Unified Entropy-based Sorting for Reversible Data Hiding.RDH schemes compete against each other for a sharply distributed prediction error histogram,usually realized by utilizing prediction strategies together with sorting technique that aims to estimate the local context complexity for each pixel to optimize the embedding order.Sorting techniques benefit prediction a lot by picking out pixels located in smooth areas.In this paper,a novel entropy-based sorting(EBS)scheme is proposed for reversible data hiding,which uses entropy measurement to characterize local context complexity for each image pixel.Futhermore,by extending the EBS technique to the two-dimensional case,it shows generalized abilities for multi-dimensinal RDH scenarios.Additionally,a new gradient-based tracking and weighting(GBTW)pixel prediction method is introduced to be combined with the EBS technique.Experimental results apparently indicate that our proposed method outperforms the previous state-of-arts counterparts significantly in terms of both the prediction accuracy and the overall embedding performance.2.Proposed Second Order Perdicting-Error Sorting for RDH.In this paper,we propose a novel second order perdicting and sorting technique for reversible data hiding.Firstly,the prediction error is obtained by an interchannel secondary prediction using the prediction errors of current channel and reference channel.Experiments show that this prediction method can produce a shaper second order prediction-error histogram.Then,we will introduce a novel second order perdicting-error sorting(SOPS)algorithm,which make full use of the feature of the edge information obtained from another color channel and high correlation between adjacent pixels.So it will reflect the texture complexity of current pixel better.Experimental results demonstrate that our proposed method outperforms the previous state-of-arts counterparts significantly in terms of both the prediction accuracy and the overall embedding performance.3.Proposed Optimal Structural Similarity Constraint for RDH.Until now.most RDH techniques have been evaluated by peak signal-to-noise ratio(Power Signal-to-Noise Ratio,PSNR),which based on mean squared error(Mean Square Error.MSE).Unfortunately,MSE turns out to be an extremely poor measure when the purpose is to predict perceived signal fidelity or quality.The structural similarity(Structural Similarity Index Measure.SSIM)index has gained widespread popularity as an alternative motivating principle for the design of image quality measures.How to utilize the characterize of SSIM to design RDH algorithm is very critical.In this paper,we propose an optimal RDH algorithm under structural similarity constraint.Firstly,we deduce the metric of the structural similarity constraint.Secondly,we construct the rate-distortion function of optimal structural similarity constraint,which is equivalent to minimize the average distortion for a given embedding rate,and then we can obtain the optimal transition probability matrix under the structural similarity constraint.Experiments show that our proposed method can be used to improve the performance of previous RDH schemes evaluated by SSIM.4.Proposed Optimal RDH Based on Earth Mover Distance.The Earth Movers Distance(EMD)is defined as the minimal cost that must be paid to transform one histogram into the other,where there is a ground distance between the basic features that are aggregated into the histogram.Based on the metric of the structural similarity constraint,we use the EMD strategy to estimate the optimal transition probability matrix.Then we construct the rate-distortion function of optimal structural similarity constraint,and then we can obtain the optimal transition probability matrix under the structural similarity constraint.Experiments show that our proposed method outperforms the state-of-arts performance in SSIM.