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Conditional Coding for Flexible Learned Video Compression

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.
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Contributor : Théo Ladune Connect in order to contact the contributor
Submitted on : Tuesday, May 4, 2021 - 4:43:22 PM
Last modification on : Friday, October 22, 2021 - 3:04:10 AM


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  • HAL Id : hal-03192548, version 3


Théo Ladune, Pierrick Philippe, Wassim Hamidouche, Lu Zhang, Olivier Déforges. Conditional Coding for Flexible Learned Video Compression. International Conference on Learning Representations (ICLR) 2021, Neural Compression Workshop, May 2021, Vienne (virtual), Austria. ⟨hal-03192548v3⟩



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