Learning What Structure to Preserve for Source-Faithful Generative Image Super-Resolution

Generative super-resolution can synthesize plausible details that do not match the source. We introduce MetaSR, a source-assisted framework that learns how to use structural metadata and which structure to transmit under a budget. The receiver encodes the LR image and edge map with a shared VAE and fuses aligned tokens through native transformer attention. We define structural utility as the change in whole-image reconstruction quality produced by an assignment, relative to MetaSR w/o edge with the same trained receiver. A lightweight sender-side predictor learns reusable region-action scores from these counterfactual outcomes. Combining the scores with measured coding costs enables budgeted selection without online receiver queries, and changing the budget requires no retraining or recalibration. Across three $\times 4$ benchmarks, MetaSR improves PSNR by 0.47–1.60 dB over MetaSR w/o edge at matched aggregate transmission rates. On a held-out evaluation set with the receiver fixed, reusing the learned scores at five unseen budgets outperforms random allocation by 0.50 dB on average under shared metadata caps. These results show that receiver-dependent utility can guide which source structure to preserve for source-faithful reconstruction.