{"ID":2847764,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00231","arxiv_id":"2511.00231","title":"Towards 1000-fold Electron Microscopy Image Compression for Connectomics via VQ-VAE with Transformer Prior","abstract":"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.","short_abstract":"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 compressio...","url_abs":"https://arxiv.org/abs/2511.00231","url_pdf":"https://arxiv.org/pdf/2511.00231v2","authors":"[\"Fuming Yang\",\"Yicong Li\",\"Hanspeter Pfister\",\"Jeff W. Lichtman\",\"Yaron Meirovitch\"]","published":"2025-10-31T20:05:21Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Transformer\",\"Variational Autoencoder\"]","has_code":false}