A Novel Prediction Error Histogram Shifting-based Reversible Data Hiding Scheme for Medical Image Transmission

Abstract

Reversible data hiding (RDH) is an actively emerging area in the domain of Information Security having wide applications in clinical data transmission along with medical images. In our research, we came up with a new RDH scheme to keep clinical data in the medical image to ensure secure data transmission. Histogram shifting-based RDH schemes are widely explored for RDH in images. The conventional histogram shifting-based RDH schemes have two major concerns: low embedding rate and overflow or underflow. In this approach, we discuss a prediction error histogram shifting-based approach with an improved overflow handling technique. The pixels in the images are divided into two different categories: black and white. The classification of the pixels has been carried out based on the checkerboard pattern. As we know that as per the checkerboard pattern, a black pixel will have four 4-neighbourhood pixels (left, right, top and bottom). To predict the black pixel value in the middle we used the average of three pixels out of 4-neighbourhood which are very close to the central pixel value. By considering the predicted pixel value and the actual pixel value, we have computed the prediction error. The histogram of prediction error is generated based on the prediction error corresponds to all the black pixels in the image. The prediction error histogram is considered for further data hiding through the histogram shifting approach. The overflow/underflow is a critical issue in the histogram shifting-based RDH scheme, so we have came up with an improved overflow/underflow handling technique in this approach. We have validated the results after carrying out the proposed scheme on medical and natural images.