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

2026-08-11 14:17:39
Jonas Brenig, Radu Timofte
arXiv_CV
     
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.

Abstract (translated)

URL

https://arxiv.org/abs/2608.10947

PDF

https://arxiv.org/pdf/2608.10947.pdf

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