mlsanityData to 14 Oct 2026

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

arXiv 2608.10947 ↗First posted 11 Aug 2026cs.CVcited by 0

On the map: Mixture-of-experts → MoE large language models

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in MoE large language models, one of 12 directions in Mixture-of-experts. Builds on 30 of its 40 references that are in the map. First posted on arXiv 11 Aug 2026.

Builds on

30 of 40 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models29 Jan
  2. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer23 Jan

Other references · 28

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  4. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  5. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
  6. 2018Variational image compression with a scale hyperpriorcited by 935
  7. 2023RWKV: Reinventing RNNs for the Transformer Eracited by 827
  8. 2021Scaling Vision with Sparse Mixture of Expertscited by 747
  9. 2018Joint Autoregressive and Hierarchical Priors for Learned Image Compressioncited by 664
  10. 2022ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Codingcited by 301
  11. 2017Lossy Image Compression with Compressive Autoencoderscited by 294
  12. 2020Channel-wise Autoregressive Entropy Models for Learned Image Compressioncited by 264
16 more
  1. 2023From Sparse to Soft Mixtures of Expertscited by 248
  2. 2023Learned Image Compression with Mixed Transformer-CNN Architecturescited by 211
  3. 2021Checkerboard Context Model for Efficient Learned Image Compressioncited by 202
  4. 2022Efficient Frequency Domain-based Transformers for High-Quality Image Deblurringcited by 165
  5. 2022The Devil Is in the Details: Window-based Attention for Image Compressioncited by 147
  6. 2022Entroformer: A Transformer-based Entropy Model for Learned Image Compressioncited by 107
  7. 2022MLIC: Multi-Reference Entropy Model for Learned Image Compressioncited by 84
  8. 2020Improving Inference for Neural Image Compressioncited by 83
  9. 2024Complexity Experts are Task-Discriminative Learners for Any Image Restorationcited by 77
  10. 2023MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compressioncited by 58
  11. 2025Learned Image Compression with Dictionary-based Entropy Modelcited by 30
  12. 2024MambaVC: Learned Visual Compression with Selective State Spacescited by 28
  13. 2025Linear Attention Modeling for Learned Image Compressioncited by 23
  14. 2025Learned Image Compression with Hierarchical Progressive Context Modelingcited by 21
  15. 2023WM-MoE: Weather-aware Multi-scale Mixture-of-Experts for Blind Adverse Weather Removalcited by 11
  16. 2025MLICv2: Enhanced Multi-Reference Entropy Modeling for Learned Image Compressioncited by 6

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026MoECodec: Image Compression for joint human and machine perception via Mixture-of-Experts
  2. 2025MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?
  3. 2025Efficient Learned Image Compression Through Knowledge Distillation
  4. 2022Post-Training Quantization for Cross-Platform Learned Image Compression
  5. 2024QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts
  6. 2024Mixture of Experts in Image Classification: What's the Sweet Spot?
  7. 2023Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference
  8. 2025Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations