# Neural Video Compression with Domain Transfer

arXiv: https://arxiv.org/abs/2605.13476 · HTML: https://mlsanity.com/paper/2605.13476/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 45 references that are in the map.
- First posted on arXiv 13 May 2026.

## Builds on (most cited first, 26 of 26)

- [Deep Contextual Video Compression](https://mlsanity.com/paper/2109.15047/) (2021)
- [Neural Video Compression with Diverse Contexts](https://mlsanity.com/paper/2302.14402/) (2023)
- [Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression](https://mlsanity.com/paper/2207.05894/) (2022)
- [Neural Video Compression with Feature Modulation](https://mlsanity.com/paper/2402.17414/) (2024)
- [Temporal Context Mining for Learned Video Compression](https://mlsanity.com/paper/2111.13850/) (2021)
- [Improving Inference for Neural Image Compression](https://mlsanity.com/paper/2006.04240/) (2020)
- [Towards Practical Real-Time Neural Video Compression](https://mlsanity.com/paper/2502.20762/) (2025)
- [Content Adaptive and Error Propagation Aware Deep Video Compression](https://mlsanity.com/paper/2003.11282/) (2020)
- [Content Adaptive Optimization for Neural Image Compression](https://mlsanity.com/paper/1906.01223/) (2019)
- [Overfitting for Fun and Profit: Instance-Adaptive Data Compression](https://mlsanity.com/paper/2101.08687/) (2021)
- [Spatial Decomposition and Temporal Fusion based Inter Prediction for Learned Video Compression](https://mlsanity.com/paper/2401.15864/) (2024)
- [Bi-Directional Deep Contextual Video Compression](https://mlsanity.com/paper/2408.08604/) (2024)
- [ECVC: Exploiting Non-Local Correlations in Multiple Frames for Contextual Video Compression](https://mlsanity.com/paper/2410.09706/) (2024)
- [Offline and Online Optical Flow Enhancement for Deep Video Compression](https://mlsanity.com/paper/2307.05092/) (2023)
- [Content Adaptive Latents and Decoder for Neural Image Compression](https://mlsanity.com/paper/2212.10132/) (2022)
- [Group-aware Parameter-efficient Updating for Content-Adaptive Neural Video Compression](https://mlsanity.com/paper/2405.04274/) (2024)
- [Universal Deep Image Compression via Content-Adaptive Optimization with Adapters](https://mlsanity.com/paper/2211.00918/) (2022)
- [BiECVC: Gated Diversification of Bidirectional Contexts for Learned Video Compression](https://mlsanity.com/paper/2505.09193/) (2025)
- [Emerging Advances in Learned Video Compression: Models, Systems and Beyond](https://mlsanity.com/paper/2504.21445/) (2025)
- [Prediction and Reference Quality Adaptation for Learned Video Compression](https://mlsanity.com/paper/2406.14118/) (2024)
- [Content-Adaptive Inference for State-of-the-art Learned Video Compression](https://mlsanity.com/paper/2510.07283/) (2025)
- [Hybrid Local-Global Context Learning for Neural Video Compression](https://mlsanity.com/paper/2412.00446/) (2024)
- [L-LBVC: Long-Term Motion Estimation and Prediction for Learned Bi-Directional Video Compression](https://mlsanity.com/paper/2504.02560/) (2025)
- [Fine-Grained Motion Compression and Selective Temporal Fusion for Neural B-Frame Video Coding](https://mlsanity.com/paper/2506.07709/) (2025)
- [L-STEC: Learned Video Compression with Long-term Spatio-Temporal Enhanced Context](https://mlsanity.com/paper/2512.12790/) (2025)
- [Content Adaptive based Motion Alignment Framework for Learned Video Compression](https://mlsanity.com/paper/2512.12936/) (2025)

## Built on by (most cited first, 1 of 1)

- [Boosting Neural Video Codec via Scale-Driven Online Flow Refinement](https://mlsanity.com/paper/2606.23023/) (2026)

## Abstract (arXiv)

Content-adaptive compression has always been a key direction in neural video coding (NVC), aiming to mitigate the domain gap between training and testing data. Such gaps often arise from distributional discrepancies between training and inference data, which may cause noticeable performance degradation when the testing content differs from the training distribution. To tackle this challenge, we propose DCVC-DT, a domain transfer enhanced neural video compression framework. Specifically, we design a lightweight online domain transfer (DT) mechanism that dynamically adapts the encoded latent representation during inference, effectively bridging the domain gap without modifying the encoder or decoder parameters. In addition, we develop a frame-level dynamic RD (Rate and Distortion) adjustment scheme that actively regulates the ratio of R and D in the loss function based on quality fluctuation, thereby improving rate-distortion performance. Extensive experiments demonstrate that DCVC-DT achieves up to 6.21% bitrate savings over the baseline DCVC-DC, while significantly enhancing generalization to unseen testing data and alleviating error propagation. Our code is available at https://github.com/SunnyMass/DCVC-DT.