A new study introduces a groundbreaking vector-quantized variational autoencoder (VQ-VAE) framework that significantly enhances electron microscopy image compression, achieving rates between 16x and 1024x. This innovation is crucial as it addresses the challenges posed by massive petascale datasets, making storage and analysis more efficient. The framework allows for flexible decoding options, which can help researchers maintain image quality while managing large data volumes, ultimately advancing the field of connectomics.