Mixture-of-Experts-based Entropy Model for Learned Image Compression

Jonas Brenig*, Radu Timofte*,
Computer Vision Lab, CAIDAS & IFI, University of Würzburg, Germany
ICIP 2026
Code

Abstract

Learned image compression has seen significant progress in recent years with the development of end-to-end learned models that achieve better compression efficiency than state-of-the-art conventional methods. Recently, Mixture of Experts (MoE) approaches have seen promising results in NLP and computer vision tasks. In this paper, we introduce the MoE approach to learned image compression. We propose a MoE-based Entropy model (MoEE) for learned image compression, allowing the model to selectively activate only the subset of parameters required for the input image. Our model achieves a BD-Rate improvement over VVC of -16.85% on the Kodak dataset.

BibTeX

@inproceedings{brenig2026moee,
  title={Mixture-of-Experts-based Entropy Model for Learned Image Compression},
  author={Jonas Brenig and Radu Timofte},
  journal={Proceedings of the IEEE International Conference on Image Processing (ICIP)},
  year={2026},
  url={https://jbrenig.github.io/ICIP26-MoEE}
}