# Towards 1000-fold Electron Microscopy Image Compression for Connectomics via VQ-VAE with Transformer Prior

arXiv: https://arxiv.org/abs/2511.00231 · HTML: https://mlsanity.com/paper/2511.00231/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 31 Oct 2025.

## Builds on (most cited first, 3 of 3)

- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [Budgeted Broadcast: An Activity-Dependent Pruning Rule for Neural Network Efficiency](https://mlsanity.com/paper/2510.01263/) (2025)

## Abstract (arXiv)

Petascale electron microscopy (EM) datasets push storage, transfer, and downstream analysis toward their current limits. We present a vector-quantized variational autoencoder-based (VQ-VAE) compression framework for EM that spans 16x to 1024x and enables pay-as-you-decode usage: top-only decoding for extreme compression, with an optional Transformer prior that predicts bottom tokens (without changing the compression ratio) to restore texture via feature-wise linear modulation (FiLM) and concatenation; we further introduce an ROI-driven workflow that performs selective high-resolution reconstruction from 1024x-compressed latents only where needed.