{"ID":2827964,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.15270","arxiv_id":"2512.15270","title":"Generative Preprocessing for Image Compression with Pre-trained Diffusion Models","abstract":"Preprocessing is a well-established technique for optimizing compression, yet existing methods are predominantly Rate-Distortion (R-D) optimized and constrained by pixel-level fidelity. This work pioneers a shift towards Rate-Perception (R-P) optimization by, for the first time, adapting a large-scale pre-trained diffusion model for compression preprocessing. We propose a two-stage framework: first, we distill the multi-step Stable Diffusion 2.1 into a compact, one-step image-to-image model using Consistent Score Identity Distillation (CiD). Second, we perform a parameter-efficient fine-tuning of the distilled model's attention modules, guided by a Rate-Perception loss and a differentiable codec surrogate. Our method seamlessly integrates with standard codecs without any modification and leverages the model's powerful generative priors to enhance texture and mitigate artifacts. Experiments show substantial R-P gains, achieving up to a 30.13% BD-rate reduction in DISTS on the Kodak dataset and delivering superior subjective visual quality.","short_abstract":"Preprocessing is a well-established technique for optimizing compression, yet existing methods are predominantly Rate-Distortion (R-D) optimized and constrained by pixel-level fidelity. This work pioneers a shift towards Rate-Perception (R-P) optimization by, for the first time, adapting a large-scale pre-trained diffu...","url_abs":"https://arxiv.org/abs/2512.15270","url_pdf":"https://arxiv.org/pdf/2512.15270v1","authors":"[\"Mengxi Guo\",\"Shijie Zhao\",\"Junlin Li\",\"Li Zhang\"]","published":"2025-12-17T10:22:11Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\",\"cs.MM\"]","methods":"[\"Diffusion Model\"]","has_code":false}