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English(EN) Dual-Constrained Diffusion Image Compression for Operational Rate-Distortion-Perception Optimization

新型扩散模型优化图像压缩的权衡

研究人员开发了一种名为双约束扩散图像压缩(DCIC)的新型图像压缩技术。该方法集成了学习型编解码器和基于扩散的解码器,利用失真和幂等性约束来联合优化保真度和感知真实感。DCIC 允许从单个比特流连续调整这两个方面的权衡,与现有方法相比,在保真度和感知质量方面均表现出更优越的性能。 AI

影响 这项研究可能带来更高效、更具感知吸引力的图像压缩技术,影响媒体存储和传输。

排序理由 该集群包含一篇详细介绍新型图像压缩方法的论文。

在 arXiv cs.CV 阅读 →

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Sanxin Jiang, Jiro Katto, Heming Sun ·

    Dual-Constrained Diffusion Image Compression for Operational Rate-Distortion-Perception Optimization

    arXiv:2606.13366v1 Announce Type: new Abstract: The rate-distortion-perception (RDP) trade-off extends classical rate--distortion theory by imposing a distributional constraint on reconstructions, providing a unified framework for neural image compression that jointly governs fid…

  2. arXiv cs.CV TIER_1 English(EN) · Heming Sun ·

    Dual-Constrained Diffusion Image Compression for Operational Rate-Distortion-Perception Optimization

    The rate-distortion-perception (RDP) trade-off extends classical rate--distortion theory by imposing a distributional constraint on reconstructions, providing a unified framework for neural image compression that jointly governs fidelity and perceptual realism. While prior work a…