Adaptive Reversible Watermarking Based on Linear Prediction for Medical Videos

TL;DR

This paper introduces an adaptive reversible watermarking technique for medical videos that leverages temporal and spatial correlations to enhance prediction accuracy, ensuring data security without compromising video quality.

Contribution

It presents the first application of a combined temporal-spatial prediction method for reversible watermarking in medical videos, improving accuracy and capacity.

Findings

High PSNR values indicating excellent video quality

Large data hiding capacity achieved

Effective protection of medical information

Abstract

Reversible video watermarking can guarantee that the watermark logo and the original frame can be recovered from the watermarked frame without any distortion. Although reversible video watermarking has successfully been applied in multimedia, its application has not been extensively explored in medical videos. Reversible watermarking in medical videos is still a challenging problem. The existing reversible video watermarking algorithms, which are based on error prediction expansion, use motion vectors for prediction. In this study, we propose an adaptive reversible watermarking method for medical videos. We suggest using temporal correlations for improving the prediction accuracy. Hence, two temporal neighbor pixels in upcoming frames are used alongside the four spatial rhombus neighboring pixels to minimize the prediction error. To the best of our knowledge, this is the first time this method is applied to medical videos. The method helps to protect patients' personal and medical information by watermarking, i.e., increase the security of Health Information Systems (HIS). Experimental results demonstrate the high quality of the proposed watermarking method based on PSNR metric and a large capacity for data hiding in medical videos.