# Edge-Aware Autoencoder Design for Real-Time Mixture-of-Experts Image Compression

arXiv: https://arxiv.org/abs/2207.12348 · HTML: https://mlsanity.com/paper/2207.12348/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Vision mixture-of-experts, one of 12 directions in Mixture-of-experts.
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 25 Jul 2022.

## Placed in

- Mixture-of-experts / [Vision mixture-of-experts](https://mlsanity.com/moe/vision-mixture-of-experts/)

## Builds on (most cited first, 5 of 5)

- [Lossy Image Compression with Compressive Autoencoders](https://mlsanity.com/paper/1703.00395/) (2017)
- [Deep Unfolding Network for Image Super-Resolution](https://mlsanity.com/paper/2003.10428/) (2020)
- [Recent Advances in Autoencoder-Based Representation Learning](https://mlsanity.com/paper/1812.05069/) (2018)
- [Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures](https://mlsanity.com/paper/1409.2574/) (2014)
- [Deep Convolutional AutoEncoder-based Lossy Image Compression](https://mlsanity.com/paper/1804.09535/) (2018)

## Built on by (most cited first, 8 of 8)

- [Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey](https://mlsanity.com/paper/2404.16356/) (2024)
- [Steered Mixture-of-Experts Autoencoder Design for Real-Time Image Modelling and Denoising](https://mlsanity.com/paper/2305.03485/) (2023)
- [Steered Mixture of Experts Regression for Image Denoising with Multi-Model-Inference](https://mlsanity.com/paper/2303.17409/) (2023)
- [Adaptive Segmentation-Based Initialization for Steered Mixture of Experts Image Regression](https://mlsanity.com/paper/2409.10101/) (2024)
- [Sparse Video Representation Using Steered Mixture-of-Experts With Global Motion Compensation](https://mlsanity.com/paper/2209.05993/) (2022)
- [Rasterized Steered Mixture of Experts for Efficient 2D Image Regression](https://mlsanity.com/paper/2510.05814/) (2025)
- [Edge-Aligned Initialization of Kernels for Steered Mixture-of-Experts](https://mlsanity.com/paper/2602.02031/) (2026)
- [MixCompress: Mixture of Experts for Variable Rate Learned Image Compression](https://mlsanity.com/paper/2607.14334/) (2026)

## Abstract (arXiv)

Steered-Mixtures-of-Experts (SMoE) models provide sparse, edge-aware representations, applicable to many use-cases in image processing. This includes denoising, super-resolution and compression of 2D- and higher dimensional pixel data. Recent works for image compression indicate that compression of images based on SMoE models can provide competitive performance to the state-of-the-art. Unfortunately, the iterative model-building process at the encoder comes with excessive computational demands. In this paper we introduce a novel edge-aware Autoencoder (AE) strategy designed to avoid the time-consuming iterative optimization of SMoE models. This is done by directly mapping pixel blocks to model parameters for compression, in spirit similar to recent works on "unfolding" of algorithms, while maintaining full compatibility to the established SMoE framework. With our plug-in AE encoder, we achieve a quantum-leap in performance with encoder run-time savings by a factor of 500 to 1000 with even improved image reconstruction quality. For image compression the plug-in AE encoder has real-time properties and improves RD-performance compared to our previous works.