用VQ-VAE+Transformer实现百万倍电子显微图像压缩，支持按需解码。
Towards 1000-fold Electron Microscopy Image Compression for Connectomics via VQ-VAE with Transformer Prior
- 基于VQ-VAE与Transformer先验的分层压缩框架
- 最高可达1024倍压缩比，仍可还原关键纹理细节
- 支持区域选择性重建，适合海量神经连接组数据
百亿级电子显微镜（EM）数据集已逼近存储、传输与下游分析的极限。本文提出一种基于向量量化变分自编码器（VQ-VAE）的EM图像压缩框架，压缩比覆盖16x至1024x，支持按需解码：仅解码顶层以实现极端压缩，通过可选的Transformer先验预测底层特征（不改变压缩比），利用特征线性调制（FiLM）与拼接恢复纹理；我们还引入基于感兴趣区域（ROI）的工作流，仅对1024x压缩后的潜在表示在必要区域进行高分辨率重建。
原文摘要 · Abstract (English)
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.
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