End-to-End Learned Image Coding: From Rate-Distortion Optimization to Practical Deployment

End-to-end learned image coding (LIC) has achieved substantial improvements in rate--distortion (R--D) performance, yet practical deployment requires addressing a broader set of system-level constraints. This article provides a deployment-oriented review of LIC, with particular attention to the transition from high-performance neural compression models to practical codecs. We first summarize the basic end-to-end coding framework, including learned transforms, quantization, entropy modeling, and R--D optimization. We then review practical extensions that are increasingly important for deployment, including variable-rate coding and rate control, integer-precision inference and cross-platform consistency, robustness and communication resilience, lightweight and hardware-aware implementation, and standardization and interoperability. To study these issues under a common setting, we further introduce TinyLIC as an open benchmark and reproducible case study for rate--distortion--complexity analysis. Experimental results illustrate the trade-offs among compression efficiency, computational complexity, numerical consistency, and robustness. In particular, TinyLIC with approximately 10 KMACs/pixel decoding complexity substantially outperforms the H.265/HEVC image-profile anchor on Kodak, while the 50 KMACs/pixel configuration achieves performance comparable to the H.266/VVC image-profile anchor on several test sets. The review highlights practical constraints and design trade-offs that remain central to the deployment and interoperability of learned image codecs. Materials are accessible at https://njuvision.github.io/TinyLIC.