Conditional Coding for Flexible Learned Video Compression

Théo Ladune, Pierrick Philippe, Wassim Hamidouche, Lu Zhang, Olivier Déforges

Keywords: Video Compression, Deep Learning, End-to-end, Auto-Encoder

TL;DR: A novel framework to learn from scratch a neural-based video coder, competitive with HEVC under different coding configurations.

Abstract: 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.