FDIR: Harmonizing Fidelity and Human-Machine Preference in Lossy Compression Image Restoration

• URL: http://arxiv.org/abs/2608.00111v1
• Date: Fri, 31 Jul 2026 08:33:40 GMT
• Title: FDIR: Harmonizing Fidelity and Human-Machine Preference in Lossy Compression Image Restoration
• Abstract summary: Flow-Conditioned Detail Refinement (FCDR) deterministically restores high-frequency textures and suppresses generative hallucinations in pixel space.<n>FDIR achieves superior fidelity, with a favorable perceptual-fidelity balance and competitive machine preference.
• Score: 3.7323617838648566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image restoration quality can be evaluated along three complementary facets: pixel-level fidelity, human perception, and downstream machine preference. However, existing lossy compression restoration methods optimize for at most one of these criteria: fidelity-oriented models often regress toward conditional means and produce over-smoothed outputs, while generative approaches hallucinate plausible but factually incorrect textures that degrade both ground-truth fidelity and downstream task accuracy. To navigate this three-way tradeoff, we propose FDIR, a two-stage architecture that decouples the conflicting demands through complementary inductive biases: Quality-Guided One-Step Flow Matching (QO-Flow) recovers global semantic structure in latent space via a single forward pass, while Flow-Conditioned Detail Refinement (FCDR) deterministically restores high-frequency textures and suppresses generative hallucinations in pixel space. Extensive experiments demonstrate that FDIR achieves superior fidelity, with a favorable perceptual-fidelity balance and competitive machine preference.

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