  This paper presents an end-to-end learning-based video compression system,
termed CANF-VC, based on conditional augmented normalizing flows (CANF). Most
learned video compression systems adopt the same hybrid-based coding
architecture as the traditional codecs. Recent research on conditional coding
has shown the sub-optimality of the hybrid-based coding and opens up
opportunities for deep generative models to take a key role in creating new
coding frameworks. CANF-VC represents a new attempt that leverages the
conditional ANF to learn a video generative model for conditional inter-frame
coding. We choose ANF because it is a special type of generative model, which
includes variational autoencoder as a special case and is able to achieve
better expressiveness. CANF-VC also extends the idea of conditional coding to
motion coding, forming a purely conditional coding framework. Extensive
experimental results on commonly used datasets confirm the superiority of
CANF-VC to the state-of-the-art methods. The source code of CANF-VC is
available at https://github.com/NYCU-MAPL/CANF-VC.
