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

• URL: http://arxiv.org/abs/2601.01141v1
• Date: Sat, 03 Jan 2026 10:12:07 GMT
• Title: YODA: Yet Another One-step Diffusion-based Video Compressor
• Authors: Xingchen Li, Junzhe Zhang, Junqi Shi, Ming Lu, Zhan Ma,
• Abstract summary: One-step diffusion models have recently excelled in perceptual image compression, their application to video remains limited.<n>We present YYet-One-step Diffusion-based Video which embeds multiscale features from temporal references for both latent generation and latent coding to better exploit spatial correlations.<n>YODA achieves state-of-the-art perceptual performance, consistently outperforming deep-learning baselines on LPIPS, DISTS, FID, and KID.
• Score: 55.356234617448905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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