The rate-distortion-perception (RDP) tradeoff characterizes the fundamental limits of lossy compression by jointly considering bitrate, reconstruction fidelity, and perceptual quality. While recent neural compression methods have improved perceptual performance, they typically operate at fixed points on the RDP surface, requiring retraining to target different tradeoffs. In this work, we propose a training-free framework that leverages pre-trained diffusion models to traverse the entire RDP surface. Our approach integrates a reverse channel coding (RCC) module with a novel score-scaled probability flow ODE decoder. We theoretically prove that the proposed diffusion decoder is optimal for the distortion-perception tradeoff under AWGN observations and that the overall framework with the RCC module achieves the optimal RDP function in the Gaussian case. Empirical results across multiple datasets demonstrate the framework's flexibility and effectiveness in navigating the ternary RDP tradeoff using pre-trained diffusion models. Our results establish a practical and theoretically grounded approach to adaptive, perception-aware compression.

翻译：率失真感知（RDP）权衡通过联合考虑比特率、重建保真度和感知质量，刻画了有损压缩的基本极限。尽管最新的神经压缩方法提升了感知性能，但它们通常在RDP曲面上的固定点运行，需要重新训练才能达到不同的权衡目标。在本工作中，我们提出了一种无训练框架，利用预训练的扩散模型遍历整个RDP曲面。我们的方法将反向信道编码（RCC）模块与新颖的分数缩放概率流ODE解码器相结合。我们从理论上证明，所提出的扩散解码器在高斯白噪声观测下对于失真-感知权衡是最优的，并且在高斯情况下，带有RCC模块的整体框架能够实现最优RDP函数。跨多个数据集的实证结果表明，该框架在使用预训练扩散模型导航三元RDP权衡时具有灵活性和有效性。我们的研究结果为自适应、感知感知压缩建立了一种实用且具有理论基础的解决方案。