CANF-VC: Conditional Augmented Normalizing Flows for Video Compression

Yung Han Ho, Chih Peng Chang, Peng Yu Chen, Alessandro Gnutti, Wen-Hsiao Peng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 suboptimality 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.
Original languageEnglish
Title of host publicationEuropean Conference on Computer Vision (ECCV), 2022
StatePublished - 2022

Publication series

NameEuropean Conference on Computer Vision


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