Controllable Generative Reference for Stereo Image Compression via Reliability-Aware Gating

Stereo image compression fundamentally relies on exploitingcross-view redundancies. We observe that the performance of conven-tional correspondence-driven methodologies degrades severely in out-of-view (OOV) regions or when reference features are heavily quantized,resulting in blurred textures and prediction failure. In this paper, we pro-pose a novel generative stereo image compression framework driven bya controllable generative reference. Instead of relying on finding explicitcorrespondences from a degraded reference, we leverage the powerful pri-ors of a pre-trained diffusion model to synthesize a high-quality target-view reference. To bridge the gap between stochastic generative processesand strict stereo consistency, we introduce Joint Semantic-Spatial Con-trol for reference generation. This mechanism synergistically steers thesynthesis using a robust geometric anchor from the compressed left viewand an ultra-compact semantic condition extracted from the target view.Furthermore, to minimize the impact of structural inconsistency duringreference generation on the quality of the reconstructed image, we de-sign a Reliability-Aware Gating module. By dynamically evaluating thesynthesized prior in the feature space, this module adaptively regulatesgenerative information flow within the entropy model to improve thereconstructed image quality. Extensive experiments on standard bench-marks demonstrate that our framework achieves state-of-the-art rate-distortion performance, delivering remarkable bitrate savings and highvisual quality at low bitrates.