🤖 AI Summary
This work addresses the inefficiency of diffusion models in lossy compression, where multi-step iterative sampling leads to slow encoding and decoding. To overcome this limitation, the authors integrate few-step generative models—such as Rectified Flow, Continuous Trajectory Matching (CTM), and MeanFlow—into the Reverse Channel Coding (RCC) framework, presenting the first probabilistic encoder-decoder formulation for these models that enables efficient compression without retraining. By leveraging velocity parameterization and denoising equivalence, they derive the posterior distribution required by RCC and further enhance CTM through EDM-inspired noise scheduling and local Gaussian approximation. Experiments demonstrate that the proposed approach substantially accelerates encoding and decoding on low-resolution benchmarks while improving perceptual quality of reconstructions at low bitrates.
📝 Abstract
DiffC provides a principled way to reuse pre-trained diffusion models for lossy compression, but its encoding and decoding procedures remain slow because they require many discretized forward and reverse steps. We study whether few-step generative models -- Rectified Flow, Consistency Trajectory Models (CTM), and MeanFlow -- can be cast as codecs within the same reverse channel coding (RCC) framework. The main challenge is that RCC requires posterior and shared distribution parameters, whereas these models do not explicitly parameterize intermediate conditional distributions. For Rectified Flow and MeanFlow, we use the equivalence between velocity parameterization and diffusion-style denoising parameterization to derive the quantities required by RCC. For CTM, which is distilled from EDM, we adopt the EDM noise parameterization together with local Gaussian approximations of the sender and shared distributions at intermediate states. This yields a proof-of-concept probabilistic formulation that enables compression with pre-trained few-step generative models without retraining. On low-resolution benchmarks, the resulting codecs reduce encoding and decoding time and improve realism in the low-bit-rate regime.