DeNC++: Efficient Diffusion-Enhanced Neural Codec for End-to-end Semantic Streaming at the Edge

Qihua Zhou; Wangjiang Gong; Zili Meng; Yaxiong Xie; Yaodong Huang; Junchen Jiang; Laizhong Cui

AAAI 2026technical0 citations

DeNC++: Efficient Diffusion-Enhanced Neural Codec for End-to-end Semantic Streaming at the Edge

Qihua Zhou, Wangjiang Gong, Zili Meng, Yaxiong Xie, Yaodong Huang, Junchen Jiang, Laizhong Cui

Abstract

The neural-enhanced video streaming (NeVS) has been an emerging technique to integrate neural models into video codecs for higher streaming efficiency. The state-of-the-art methods, e.g., DeNC and Gemino, typically compress videos in RGB space and restore video quality via a neural enhancement model hosted on the external media server. However, these methods are not always accessible in resource-constrained edge environments due to their heavy reliance on the media server

BibTeX

@inproceedings{aaai2026_dencefficientdif,
  title = {DeNC++: Efficient Diffusion-Enhanced Neural Codec for End-to-end Semantic Streaming at the Edge},
  author = {Qihua Zhou and Wangjiang Gong and Zili Meng and Yaxiong Xie and Yaodong Huang and Junchen Jiang and Laizhong Cui},
  booktitle = {AAAI 2026},
  year = {2026}
}