Generative Latent Diffusion for Efficient Spatiotemporal Data Reduction
This work addresses storage cost reduction for spatiotemporal data compression, offering a novel method that improves upon existing approaches, though it is incremental in combining variational autoencoders with conditional diffusion models.
The paper tackles the problem of limited controllability and reconstruction accuracy in generative models for data compression by proposing an efficient latent diffusion framework that compresses keyframes into latent space and reconstructs remaining frames via generative interpolation. Experimental results show up to 10 times higher compression ratios than rule-based state-of-the-art compressors and up to 63 percent better performance than leading learning-based methods under the same reconstruction error.
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.