Heterogeneous architecture for reversible watermarking system for medical images using Integer transform based Reverse Contrast Mapping (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Algorithms in multimedia security including machine learning and deep learning find its way into more and more application field resulting in strict power and area constraints. This challenges traditional homogeneous computing concepts and drives the development of new, heterogeneous architectures. One way to attain a balance of high data throughput and flexibility is to combine soft-core FPGA accelerators with CPUs as hosts. In this paper, in order to achieve higher performance, low latency, unlimited re-configurability and most important very high energy efficiency, we have proposed a hybrid architecture using CPU and FPGA. These components are then dynamically configured to form complex algorithms directly in hardware by implementing reversible watermarking of medical images. We are proposing such reversible watermarking using Integer transform based Reversible Contrast Mapping (RCM) algorithm. We got improvement in results with respect to Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM), when compare to the implementation in proposed architecture and simulation results of different image formats like jpeg, png and DICOM.