Training-Free Rate-Distortion-Perception Traversal With Diffusion
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 ...
| Authors | Yuhan Wang, Suzhi Bi, Ying-Jun Angela Zhang |
|---|---|
| Primary category | cs.IT |
| Published | 4 March 2026 |
| Last updated at source | 22 May 2026 |
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