README.md

---
language:
- en
license: bsd-3-clause-clear
pipeline_tag: video-to-video
---

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<div align="center">

# **NEVC-1.0** <br>(EHVC: Efficient Hierarchical Reference and Quality Structure for Neural Video Coding)

[📄 Paper](https://huggingface.co/papers/2509.04118)

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<img src="./assets/performance.png" alt="Performance comparison" width="60%" style="max-width: 100%;" height="auto">
</div>

</div>

<div align="left">

## 📝 Introduction
This repository provides the pretrained model weights for **NEVC-1.0**, which integrates contributions from **EHVC (Efficient Hierarchical Reference and Quality Structure for Neural Video Coding)** — one of the core components of the framework.  
**EHVC** introduces a hierarchical reference and quality structure that significantly improves both compression efficiency and rate–distortion performance.  
The corresponding code repository can be found here: [NEVC-1.0-EHVC](https://github.com/bytedance/NEVC).

## 📄 Paper Abstract
Neural video codecs (NVCs), leveraging the power of end-to-end learning, have demonstrated remarkable coding efficiency improvements over traditional video codecs. Recent research has begun to pay attention to the quality structures in NVCs, optimizing them by introducing explicit hierarchical designs. However, less attention has been paid to the reference structure design, which fundamentally should be aligned with the hierarchical quality structure. In addition, there is still significant room for further optimization of the hierarchical quality structure. To address these challenges in NVCs, we propose EHVC, an efficient hierarchical neural video codec featuring three key innovations: (1) a hierarchical multi-reference scheme that draws on traditional video codec design to align reference and quality structures, thereby addressing the reference-quality mismatch; (2) a lookahead strategy to utilize an encoder-side context from future frames to enhance the quality structure; (3) a layer-wise quality scale with random quality training strategy to stabilize quality structures during inference. With these improvements, EHVC achieves significantly superior performance to the state-of-the-art NVCs. Code will be released in: this https URL .

Key designs of **EHVC** include:
- **Hierarchical multi-reference:** Resolves reference–quality mismatches using a hierarchical reference structure and a multi-reference scheme, optimized for low-delay configurations.  
- **Lookahead mechanism:** Enhances encoder-side context by leveraging forward features, thereby improving prediction accuracy and compression.  
- **Layer-wise quantization scale with random quality training:** Provides a flexible and efficient quality structure that adapts during training, resulting in improved encoding performance.  

---

## 🔧 Models
EHVC uses two models: the intra model and the inter model.  
- The **intra model** handles intra-frame coding.  
- The **inter model** is responsible for inter-frame (predictive) coding.

### Intra Model
The main contributions of NEVC-1.0 focus on inter coding.  
For intra coding, we directly adopt the pretrained model **`cvpr2023_image_psnr.pth.tar`** from [DCVC-DC](https://github.com/microsoft/DCVC/blob/main/DCVC-family/DCVC-DC/checkpoints/download.py), without further training.

### Inter Model
The inter model of NEVC-1.0 is provided at **`/models/nevc1.0_inter.pth.tar`**.  
The architecture of the inter model is illustrated below:

<div align="center">
<img src="./assets/architecture.png" alt="Inter model architecture" width="50%" style="max-width: 100%;" height="auto">
</div>

---

##  📊  Experimental Results
### Objective Comparison
<div align="center">

**BD-Rate (%) comparison for PSNR**  
Anchor: VTM-23.4 LDB.  
All codecs tested with 96 frames and intra-period = 32.  

<img src="./assets/96F32G.png" alt="BD-Rate 96F32G" width="50%" style="max-width: 100%;" height="auto">

**Rate–Distortion curves** on HEVC B, HEVC C, UVG, and MCL-JCV datasets.  
Tested with 96 frames and intra-period = 32.  

<img src="./assets/96F32G_curve.png" alt="RD curves 96F32G" width="80%" style="max-width: 100%;" height="auto">

**BD-Rate (%) comparison for PSNR**  
Anchor: VTM-23.4 LDB.  
All codecs tested with full sequences and intra-period = -1.  

<img src="./assets/allF-1G.png" alt="BD-Rate allF-1G" width="50%" style="max-width: 100%;" height="auto">

**Rate–Distortion curves** on HEVC B, HEVC C, UVG, and MCL-JCV datasets.  
Tested with full sequences and intra-period = -1.  

<img src="./assets/allF-1G_curve.png" alt="RD curves allF-1G" width="80%" style="max-width: 100%;" height="auto">

</div>

---

## 📜 Citation
If you find **NEVC-1.0** useful in your research or projects, please cite the following paper:

- **EHVC: Efficient Hierarchical Reference and Quality Structure for Neural Video Coding**  
  Junqi Liao, Yaojun Wu, Chaoyi Lin, Zhipin Deng, Li Li, Dong Liu, Xiaoyan Sun.  
  *Proceedings of the 33rd ACM International Conference on Multimedia (ACM MM 2025).*  

```bibtex
@inproceedings{liao2025ehvc,
  title={EHVC: Efficient Hierarchical Reference and Quality Structure for Neural Video Coding},
  author={Liao, Junqi and Wu, Yaojun and Lin, Chaoyi and Deng, Zhipin and Li, Li and Liu, Dong and Sun, Xiaoyan},
  booktitle={Proceedings of the 33rd ACM International Conference on Multimedia},
  year={2025}
}
```

---


## 🙌 Acknowledgement
The intra model of this project is based on [DCVC-DC](https://github.com/microsoft/DCVC/blob/main/DCVC-family/DCVC-DC/checkpoints/download.py).