Machine Learning Based Efficient QT-MTT Partitioning Scheme for VVC Intra Encoders
Author:
Affiliation:
1. CNRS, INSA Rennes, IETR–UMR 6164, University Rennes, Rennes, France
2. Unit of Computing Sciences, Tampere University, Tampere, Finland
3. InterDigital, Cesson-Sévigné, France
Funder
Hubert Curien Partnerships (PHC) Maghreb 2021,
Région Bretagne through the TRISTRAM Collaborative Project
Allocations de Recherche Doctorale (ARED) Program
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Media Technology
Link
http://xplorestaging.ieee.org/ielx7/76/10207864/10004946.pdf?arnumber=10004946
Reference50 articles.
1. Effective CU Size Decision for HEVC Intracoding
2. Machine Learning Based Choice of Characteristics for the One-Shot Determination of the HEVC Intra Coding Tree
3. Speeding Up VP9 Intra Encoder With Hierarchical Deep Learning-Based Partition Prediction
4. Adaptive Inter-Mode Decision for HEVC Jointly Utilizing Inter-Level and Spatiotemporal Correlations
5. Accelerate CTU Partition to Real Time for HEVC Encoding With Complexity Control
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