NEVC - Neural Efficient Video Coding

September 5, 2025 ยท View on GitHub

NEVC - Neural Efficient Video Coding

NEVC is a neural video coding framework designed for highly efficient video compression. By integrating cutting-edge neural network models, NEVC pushes the boundaries of encoding performance and efficiency.

This repository provides access to code, pretrained models, and research papers for various versions of NEVC.


:newspaper: Release Notes

NEVC-1.0 - First Release

  • Release Date: Sep 5th, 2025
  • Key Features:
    • This version implements the core concepts and methods from the paper "EHVC: Efficient Hierarchical Reference and Quality Structure for Neural Video Coding", accepted at ACM MM 2025.

:memo: Codec Versions Overview

The following table provides details on different versions of the NEVC codec, along with associated papers, code, and checkpoints.

CodecPaperCodeCheckpoint
NEVC-1.0 (EHVC)EHVC paperEHVC codeEHVC checkpoint

:dart: Project Goals

  • Develop a neural-based video codec that offers efficient video compression.
  • Achieve major enhancements in encoding performance and compression efficiency.

:book: Citation

If you find NEVC or any part of this repository helpful in your research or projects, we kindly ask you to consider citing the following papers:

  • 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, 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}
    }
    

:scroll: License

NEVC is licensed under the BSD 3-Clause Clear License