标题： DiffVC-OSD：一步扩散式感知神经视频压缩框架 显示英文标题

标题： DiffVC-OSD: One-Step Diffusion-based Perceptual Neural Video Compression Framework

摘要： 在本工作中，我们首先提出DiffVC-OSD，一种单步扩散的感知神经视频压缩框架。 与传统的多步扩散方法不同，DiffVC-OSD将重建的潜在表示直接输入到单步扩散模型中，通过时间上下文和潜在表示本身的引导，提高感知质量。 为了更好地利用时间依赖性，我们设计了一个时间上下文适配器，将条件输入编码为多级特征，为去噪U-Net提供更细粒度的指导。 此外，我们采用端到端微调策略以提高整体压缩性能。 大量实验表明，DiffVC-OSD实现了最先进的感知压缩性能，相比对应的多步扩散变体，解码速度提高了约20$\times$，比特率降低了86.92%。 显示英文摘要

摘要： 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.