DNVC-FC: A Low-Latency Distributed Neural Video Codec for Resource-Constrained Multimedia Applications
Yiming Ding, Jianguo Wei · 2025
High-quality, low-latency video compression is essential for real-time multimedia applications, particularly in resource-constrained edge computing scenarios and large-scale systems. However, improving rate-distortion (RD) performance in neural video codecs (NVCs) often increases encoding complexity, hindering their adoption in latency-sensitive applications such as real-time video retrieval, robot navigation, and large-scale video indexing. To address this, we propose a novel distributed neural video codec (DNVC) that significantly improves RD performance while reducing encoder-side complexity. Our approach introduces a novel feature-channel conditional coding paradigm, integrating two key components: (1) a feature-channel conditioned entropy model that leverages implicit feature extraction to capture complex patterns and exploits cross-channel dependencies for efficient compression; (2) a high-precision side information generator that enables low-latency encoding and enhances decoder-side information quality by leveraging multi-frame reference. Experimental results show that our DNVC outperforms current state-of-the-art (SOTA) distributed video codecs and several NVCs in RD performance. Specifically, our codec achieves an average PSNR improvement of 1.2 dB compared to the current SOTA DNVC and 2 dB more than the widely-used H.264 codec. In low-latency scenarios, our method achieves a 5.5× to 14.3× speedup in encoding compared to previous SOTA NVCs.