Research On Reversible Information Hiding Algorithm Based On Prediction Error Expansion

This thesis mainly deals with the reversible data hiding algorithm with high capacity and low distortion.Two reversible data hiding methods based on prediction error expansion are proposed.The main work done in this thesis is as follows:Firstly,reversible data hiding algorithm improved by the regularized least squares prediction method is proposed.It effectively solves the problem of least fitting and low precision of the least square prediction algorithm in reversible data hiding.This algorithm improves the prediction accuracy of pixels.At the same time,the one dimension prediction error extended histogram is extended to the two-dimensional space by using the two-dimensional prediction error extension pattern.That is to say the prediction error for both teams in order to make the prediction error present in two-dimensional space.When data is embedded,the method of extending and shifting can be moved in the direction of smaller pixels.In this way,the image distortion can be reduced while the capacity information is embedded in the same way.The visual quality and the peak signal to noise ratio value of the image can be further improved.And we use the sorting algorithm to arrange the complexity of the pixels in ascending order.The low complexity of the prediction error gives priority to the embedding of information,which further reduces the distortion of the image.The experimental results show that the prediction accuracy of the improved algorithm is improved compared with the other reversible data hiding algorithms.At the same time,the performance of the image's capacity against distortion is improved.Secondly,based on the prediction error expansion framework,a new self-adaptive iterative prediction method is proposed to improve the prediction accuracy of the pixel in reversible data hiding algorithm.The prediction accuracy of the image texture area is improved.Based on the size of the first order difference edge preserving operator of the pixel can represent the different forms of the image,so the first order difference edge preserving operator is used in the process of image prediction.In this way,different regions of the image will use different constraints.The correlation constraints are enhanced in the image smooth region which can make the regularization enhanced.In the edge region of the image,the constraint is reduced to weaken the regularization.This paper adaptively adjusts the edge preserving operators by using the iterative method.The result of the prediction is iteratively solved,so that the results of each time are corrected.More accurate and stable solutions are further obtained.The experimental results show that the prediction accuracy of the algorithm is improved.In addition,the correlation complexity of the first order difference preserving operator of each pixel obtained by the algorithm is calculated.The complexity of pixels is sorted from small to large.First,the pixels in the smooth area are embedded in the information.This can further improve the quality of the constipation image.Finally,the experimental results show that the algorithm improves the accuracy of the predicted pixels,and improves the peak signal to noise ratio of the embedded secret information image.It effectively reduces the distortion of the carrier image.