We propose a very simple and efficient video compression framework that only focuses on
modeling the conditional entropy between frames. Unlike prior learning-based approaches,
we reduce complexity by not performing any form of explicit transformations between frames
and assume each frame is encoded with an independent state-of-the-art deep image
compressor. We first show that a simple architecture modeling the entropy between the
image latent codes is as competitive as other neural video compression works and video …

Abstract Systems and method for video compression using conditional entropy coding. An
ordered sequence of image frames can be transformed to produce an entropy coding for
each image frame. Each of the entropy codings provide a compressed form of image
information based on a prior image frame and a current image frame (the current image
frame occurring after the prior image frame). In this manner, the compression model can
capture temporal relationships between image frames or encoded representations of the …