Light field (LF) imaging captures both spatial and angular information, which is essential for applications such as depth estimation, view synthesis, and post-capture refocusing. However, the efficient processing of this data, particularly in terms of compression and encoding/decoding time, presents challenges. We propose a Variational Autoencoder (VAE)-based framework to disentangle the spatial and angular features of light field images, focusing on fast and efficient compression. Our method uses two separate sub-encoders-one for spatial and one for angular features-to allow for independent processing in the latent space, which facilitates more streamlined compression workflows. Evaluations on standard light field datasets demonstrate that our approach reduces encoding and decoding time significantly, with a slight trade-off in Rate-Distortion (RD) performance, making it suitable for real-time applications.