HEP-DHMI: The High Efficiency and Payload Data Hiding Scheme for AMBTC Compressed Medical Images

Current AMBTC-based data hiding schemes generally exhibit strong performance with general images, but their effectiveness varies significantly when applied to medical images in terms of hiding capacity, stego image quality, and efficiency. Moreover, maintaining structural similarity between the original and reconstructed images remains challenging due to the lossy nature of AMBTC compression. Given the increasing adoption of telemedicine applications, especially in the wake of Covid-19, where Internet infrastructure varies across regions, AMBTC's efficiency and ease of implementation make it well-suited for such applications. To enhance hiding capacity while preserving image quality and structural similarity, we propose a high-efficiency, high-payload data hiding scheme tailored for AMBTC-based medical images, termed HEP-DHMI. This approach involves classifying AMBTC compressed blocks into four types—flat, smooth, moderate, and complex—and designing distinct data hiding strategies for each block type. Experimental results validate that HEP-DHMI achieves an average PSNR of 34.25 dB, a payload capacity of 245,788 bits, an efficiency rating of 45.22, and an M-SSIM of 0.98 when evaluated on images selected from two prominent databases: Covid-19 and Brain Tumor MRI. These results demonstrate superior performance compared to existing schemes.