Calibrating Pretrained Generative Models for Practical Reverse-Channel Coding

Pretrained diffusion and flow models can be used for image compression through reverse-channel coding (RCC), but mismatch between the deployed receiver and the source-marginalized ideal receiver can increase communication cost. We decompose ideal expected RCC cost into required source information and receiver-mismatch excess, identifying the part that calibration can reduce. This analysis leads to Calibrated Reverse-Channel Coding (CalRCC), which calibrates prediction, confidence, and path. Prediction balances coding cost and reconstruction quality, confidence adjusts receiver uncertainty, and path reallocates model evaluations along the reverse process. Experiments with controlled DiffC show that CalRCC substantially improves practical compression, with stronger fidelity and perceptual quality without training a new generative backbone. Studies with CoD and Turbo-DDCM further suggest that the principle of prediction calibration can extend across distinct diffusion codec designs. These results show that receiver calibration provides an effective way to adapt pretrained generative models for practical compression.