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arXiv · 2511.01590

EV-NVC: Efficient Variable bitrate Neural Video Compression

Yongcun Hu·Yingzhen Zhai·Jixiang Luo·Wenrui Dai·Dell Zhang·Hongkai Xiong·Xuelong Li
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Abstract

Training neural video codec (NVC) with variable rate is a highly challenging task due to its complex training strategies and model structure. In this paper, we train an efficient variable bitrate neural video codec (EV-NVC) with the piecewise linear sampler (PLS) to improve the rate-distortion performance in high bitrate range, and the long-short-term feature fusion module (LSTFFM) to enhance the context modeling. Besides, we introduce mixed-precision training and discuss the different training strategies for each stage in detail to fully evaluate its effectiveness. Experimental results show that our approach reduces the BD-rate by 30.56% compared to HM-16.25 within low-delay mode.

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Yongcun Hu, Yingzhen Zhai, Jixiang Luo, Wenrui Dai, Dell Zhang, Hongkai Xiong, Xuelong Li. 2025-11-03. EV-NVC: Efficient Variable bitrate Neural Video Compression. https://arxiv.org/abs/2511.01590

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