Security and Real-Time FPGA Integration for Learned Image Compression

Learned Image Compression (LIC) is gaining trac- tion nowadays, yet real-time performance and secure operations on hardware platforms remain challenging. This work addresses both challenges by presenting an integrated workflow for train- ing, securing, and deploying LIC models on hardware. To achieve a hardware-efficient LIC model, we employ an iterative pruning and quantization process within a standard end-to-end learning framework. Additionally, we introduce Quantization-Aware Wa- termarking (QAW), a novel technique that embeds a watermark during quantization via a joint loss function, ensuring model integrity and security without degrading video performance. The watermarked weights undergo public-key encryption, enhancing protection by safeguarding both content and user traceability. We evaluate real-time performance, latency, energy consumption, and compression efficiency across two Field Programmable Gate Array (FPGA) platforms, showing that the watermarking and encryption steps introduce minimal overhead, PSNR decreases by 0.2 dB on average, energy consumption increases by 2%, and FPS drops by 6% on average while maintaining real-time constraints and security. Furthermore, our approach outperforms existing hardware-based LIC implementations in FPS and energy efficiency, delivering optimized LIC codecs for HD, FHD, and UHD resolutions at 61, 24, and 14 FPS, respectively