Video Compression With Rate-Distortion Autoencoders

Understand

In this paper we present a a deep generative model for lossy video compression.

• We employ a model that consists of a 3D autoencoder with a discrete latent space and an autoregressive prior used for entropy coding.
• Both autoencoder and prior are trained jointly to minimize a rate-distortion loss, which is closely related to the ELBO used in variational autoencoders.
• Despite its simplicity, we find that our method outperforms the state-of-the-art learned video compression networks based on motion compensation or interpolation.