Real-Time Neural Video Compression with Unified Intra and Inter Coding

• URL: http://arxiv.org/abs/2510.14431v4
• Date: Tue, 04 Nov 2025 03:19:02 GMT
• Title: Real-Time Neural Video Compression with Unified Intra and Inter Coding
• Authors: Hui Xiang, Yifan Bian, Li Li, Jingran Wu, Xianguo Zhang, Dong Liu,
• Abstract summary: We present an NVC framework with unified intra and inter coding, where every frame is processed by a single model.<n>We propose a simultaneous two-frame compression design to exploit interframe redundancy not only forwardly but also backwardly.<n>Our scheme outperforms DCVC-RT by an average of 12.1% BD-rate reduction, delivers more stable and quality per frame, and retains real-time encoding/decoding performances.
• Score: 8.998142257336674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural video compression (NVC) technologies have advanced rapidly in recent years, yielding state-of-the-art schemes such as DCVC-RT that offer superior compression efficiency to H.266/VVC and real-time encoding/decoding capabilities. Nonetheless, existing NVC schemes have several limitations, including inefficiency in dealing with disocclusion and new content, interframe error propagation and accumulation, among others. To eliminate these limitations, we borrow the idea from classic video coding schemes, which allow intra coding within inter-coded frames. With the intra coding tool enabled, disocclusion and new content are properly handled, and interframe error propagation is naturally intercepted without the need for manual refresh mechanisms. We present an NVC framework with unified intra and inter coding, where every frame is processed by a single model that is trained to perform intra/inter coding adaptively. Moreover, we propose a simultaneous two-frame compression design to exploit interframe redundancy not only forwardly but also backwardly. Experimental results show that our scheme outperforms DCVC-RT by an average of 12.1% BD-rate reduction, delivers more stable bitrate and quality per frame, and retains real-time encoding/decoding performances. Code and models will be released.

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