2025/08/11 by Wenzhuo Ma, Zhenzhong Chen, Ma, Wenzhuo +1 · 1 citation
Computer Science · #Advanced Data Compression Techniques #Image and Video Quality Assessment #Video Coding and Compression Technologies
paper · pdf · doi:10.48550/arxiv.2508.07682
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× faster decoding and a 86.92% bitrate reduction compared to the corresponding multi-step diffusion-based variant.