Thinking-then-Compress with Perception-Informed Spatial Budgeting for Generative Image Compression

Generative image compression introduces a generative model at the decoder to fill in details missing from the bitstream using image priors, and has become a prominent approach for perceptual compression at low bitrates. However, when a large generative decoder cannot be jointly optimized with the encoder, the encoder's compression decisions are no longer directly adapted to the decoder's reconstruction behavior. To address this issue, we propose Thinking-then-Compress, a closed-loop image compression paradigm instantiated as encoder-side search over transmitted discrete spatial schemes using reconstruction feedback from a fixed deployed decoder. The encoder first analyzes perceptually important content, proposes a regional scheme, evaluates the resulting reconstruction, and revises the scheme within a fixed feedback budget. Within this paradigm, Perception-Informed Spatial Budgeting proposes regional tier updates from image importance, codec error, and observed generative recovery, then verifies joint proposals using reconstruction feedback and their rate cost. Reconstruction-Aware Generative Enhancement further fine-tunes the generation branch and generation-side adapters for compressed inputs and uses reconstruction feedback to compose codec and generative outputs spatially, preserving reliable codec details while introducing generation where it improves recovery. Under the reported evaluation protocol, our method achieves favorable performance against existing perceptual image compression methods. Our project will be publicly available after publication.