README.md

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<div align="center">

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

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

</div>

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## 📝 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).

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.  

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## 🔧 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>

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##  📊  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>

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## 📜 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}
}
```

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## 🙌 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).