📝 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.
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, 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:
📊 Experimental Results
Objective Comparison
BD-Rate (%) comparison for PSNR
Anchor: VTM-23.4 LDB.
All codecs tested with 96 frames and intra-period = 32.
Rate–Distortion curves on HEVC B, HEVC C, UVG, and MCL-JCV datasets.
Tested with 96 frames and intra-period = 32.
BD-Rate (%) comparison for PSNR
Anchor: VTM-23.4 LDB.
All codecs tested with full sequences and intra-period = -1.
Rate–Distortion curves on HEVC B, HEVC C, UVG, and MCL-JCV datasets.
Tested with full sequences and intra-period = -1.
📜 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).
@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.