  This paper introduces a novel framework for end-to-end learned video coding.
Image compression is generalized through conditional coding to exploit
information from reference frames, allowing to process intra and inter frames
with the same coder. The system is trained through the minimization of a
rate-distortion cost, with no pre-training or proxy loss. Its flexibility is
assessed under three coding configurations (All Intra, Low-delay P and Random
Access), where it is shown to achieve performance competitive with the
state-of-the-art video codec HEVC.
