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arXiv · 2601.01141

YODA: Yet Another One-step Diffusion-based Video Compressor

Xingchen Li·Junzhe Zhang·Junqi Shi·Ming Lu·Zhan Ma
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Abstract

While one-step diffusion models have recently excelled in perceptual image compression, their application to video remains limited. Prior efforts typically rely on pretrained 2D autoencoders that generate per-frame latent representations independently, thereby neglecting temporal dependencies. We present YODA--Yet Another One-step Diffusion-based Video Compressor--which embeds multiscale features from temporal references for both latent generation and latent coding to better exploit spatial-temporal correlations for more compact representation, and employs a linear Diffusion Transformer (DiT) for efficient one-step denoising. YODA achieves state-of-the-art perceptual performance, consistently outperforming traditional and deep-learning baselines on LPIPS, DISTS, FID, and KID. Source code will be publicly available at https://github.com/NJUVISION/YODA.

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Xingchen Li, Junzhe Zhang, Junqi Shi, Ming Lu, Zhan Ma. 2026-01-03. YODA: Yet Another One-step Diffusion-based Video Compressor. https://arxiv.org/abs/2601.01141

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