Hao Cao, Yinhuan Huang, Wenqi Guo, Zhijin Qin, Jungong Han · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29691 · open-access PDF
生成图像压缩(GIC)在低比率下提供强大的感知质量,然而,具有强大的感知质量的GIC方法往往依赖于具有高延迟的预训练式扩散模型,而最近的轻量化GIC方法减少延迟,但提供较低的感知质量。我们观察到,对于轻量化GIC方法在低比率下,粗糙的确定性量化可以使接近的编码器延迟相应于不同的细节到同一点,使这些细节难以恢复。
Machine translation (Meta M2M100); the English original follows.
Generative image compression (GIC) delivers strong perceptual quality at low bitrates. However, GIC methods with strong perceptual quality often rely on pretrained diffusion models with high latency, while recent lightweight GIC methods reduce latency but deliver lower perceptual quality. We observe that, for lightweight GIC methods at low bitrates, coarse deterministic quantization can collapse nearby encoder latents corresponding to different fine details to the same point, making these details difficult to recover. Motivated by this, we propose GR-GIC, an efficient GIC that delivers competi
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