{"ID":2883582,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.07682","arxiv_id":"2508.07682","title":"DiffVC-OSD: One-Step Diffusion-based Perceptual Neural Video Compression Framework","abstract":"In this work, we first propose DiffVC-OSD, a One-Step Diffusion-based Perceptual Neural Video Compression framework. Unlike conventional multi-step diffusion-based methods, DiffVC-OSD feeds the reconstructed latent representation directly into a One-Step Diffusion Model, enhancing perceptual quality through a single diffusion step guided by both temporal context and the latent itself. To better leverage temporal dependencies, we design a Temporal Context Adapter that encodes conditional inputs into multi-level features, offering more fine-grained guidance for the Denoising Unet. Additionally, we employ an End-to-End Finetuning strategy to improve overall compression performance. Extensive experiments demonstrate that DiffVC-OSD achieves state-of-the-art perceptual compression performance, offers about 20$\\times$ faster decoding and a 86.92\\% bitrate reduction compared to the corresponding multi-step diffusion-based variant.","short_abstract":"In this work, we first propose DiffVC-OSD, a One-Step Diffusion-based Perceptual Neural Video Compression framework. Unlike conventional multi-step diffusion-based methods, DiffVC-OSD feeds the reconstructed latent representation directly into a One-Step Diffusion Model, enhancing perceptual quality through a single di...","url_abs":"https://arxiv.org/abs/2508.07682","url_pdf":"https://arxiv.org/pdf/2508.07682v1","authors":"[\"Wenzhuo Ma\",\"Zhenzhong Chen\"]","published":"2025-08-11T06:59:23Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}