EFVC: Error-Propagation-Free Neural Video Coding with Reversible Transform
Junqi Liao, Li Li, Dong Liu, Houqiang Li · 2025
Neural video codecs (NVCs) have received more and more attention. Although NVCs’ performance shows a trend of surpassing traditional video coding, challenges still hinder their practical application. One significant challenge is the error propagation in long prediction chains. Many existing NVCs alleviate the error propagation but do not solve it. In this paper, we analyze that the causes of error propagation are train-test mismatch and explicit hierarchical quality scheme absence in NVC. Moreover, in response to the Grand Challenge on Neural Network-based Video Coding at ISCAS 2025, we propose the first error-propagation-free NVC, EFVC. The EFVC introduces a reversible transform backbone to eliminate the unstable network information loss caused by train-test mismatch. Then, a hierarchical quality strategy is introduced to constrain hierarchical quality explicitly. Furthermore, to eliminate the error propagation further, we restrict the nonlinearity. The experimental results show that EFVC effectively eliminates error propagation. In addition, our proposed EFVC leads to a 21.8% BD-rate reduction on average compared with the reproduced SOTA NVC, DCVC-DC.