Generative Latent Diffusion for Efficient Spatiotemporal Data Reduction

TL;DR

This paper introduces a latent diffusion-based data compression method that efficiently reconstructs spatiotemporal data by storing only keyframes, achieving significantly higher compression ratios and better reconstruction quality than existing methods.

Contribution

It proposes a novel latent diffusion framework combining autoencoders and diffusion models for efficient spatiotemporal data compression and reconstruction.

Findings

Achieves up to 10x higher compression ratios than SZ3.

Outperforms leading learning-based methods by 63% at the same error level.

Effectively reconstructs data using minimal keyframes and generative interpolation.

Abstract

Generative models have demonstrated strong performance in conditional settings and can be viewed as a form of data compression, where the condition serves as a compact representation. However, their limited controllability and reconstruction accuracy restrict their practical application to data compression. In this work, we propose an efficient latent diffusion framework that bridges this gap by combining a variational autoencoder with a conditional diffusion model. Our method compresses only a small number of keyframes into latent space and uses them as conditioning inputs to reconstruct the remaining frames via generative interpolation, eliminating the need to store latent representations for every frame. This approach enables accurate spatiotemporal reconstruction while significantly reducing storage costs. Experimental results across multiple datasets show that our method achieves up to 10 times higher compression ratios than rule-based state-of-the-art compressors such as SZ3, and up to 63 percent better performance than leading learning-based methods under the same reconstruction error.