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

Jonas Brenig; Radu Timofte

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arXiv · 2608.10947

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

Jonas Brenig·Radu Timofte

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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.

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BibTeX RIS

Jonas Brenig, Radu Timofte. 2026-08-11. Mixture-of-Experts-based Entropy Model for Learned Image Compression. https://arxiv.org/abs/2608.10947

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