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End-to-end learning-based video compression has made steady progress over thelast several years. However, unlike learning-based image coding, which hasalready surpassed its handcrafted counterparts, learning-based video codingstill has some ways to go. In this paper we present learned conditional codingmodes for video coding (LCCM-VC), a video coding model that achievesstate-of-the-art results among learning-based video coding methods. Our modelutilizes conditional coding engines from the recent conditional augmentednormalizing flows (CANF) pipeline, and introduces additional coding modes toimprove compression performance. The compression efficiency is especially goodin the high-quality/high-bitrate range, which is important for broadcast andvideo-on-demand streaming applications. The implementation of LCCM-VC isavailable at https://github.com/hadihdz/lccm_vc

LCCM-VC: Learned Conditional Coding Modes for Video Compression