Publication: INCEPT: Intra CU Depth Prediction for H...
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Title: | INCEPT: Intra CU Depth Prediction for HEVC |
Subtitle: | |
Abstract: | High Efficiency Video Coding (HEVC) improves the encoding efficiency by utilizing sophisticated tools such as flexible Coding Tree Units (CTUs) partitioning. The Coding Units (CUs) can be split recursively into four equally sized CUs ranging from 64×64 to 8×8 pixels. At each depth level (or CU size), intra prediction via exhaustive mode search was exploited in HEVC to improve the encoding efficiency and result in a very high encoding time complexity. This paper proposes an Intra CU Depth Prediction (INCEPT) algorithm, which limits Rate-Distortion Optimization (RDO) for each CTU in HEVC by utilizing the spatial correlation with the neighboring CTUs, which is computed using a DCT energy-based feature. Thus, INCEPT reduces the number of candidate CU sizes required to be considered for each CTU in HEVC intra coding. Experimental results show that the INCEPT algorithm achieves a better trade-off between the encoding efficiency and encoding time saving (i.e., BDR/∆T) than the benchmark algorithms. While BDR/∆T is 12.35% and 9.03% for the benchmark algorithms, it is 5.49% for the proposed algorithm. As a result, INCEPT achieves a 23.34% reduction in encoding time on average while incurring only a 1.67% increase in bitrate than the original coding in the x265 HEVC open-source encoder. |
Keywords: | HEVC, Intra coding, CTU, CU, depth decision |
Publication type: | Article in Proceedings (Authorship) |
Publication date: | 06.10.2021 (Print) |
Published by: |
MMSP 2021 Proceedings of the IEEE 23rd International Workshop on Multimedia Signal Processing
MMSP 2021 Proceedings of the IEEE 23rd International Workshop on Multimedia Signal Processing
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IEEE;
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to publication |
Title of the series: | - |
Volume number: | - |
First publication: | Yes |
Version: | - |
Page: | pp. 1 - 6 |
Versionen
Keine Version vorhanden |
Publication date: | 06.10.2021 |
ISBN: |
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ISSN: | 2163-3517 |
Homepage: | https://ieeexplore.ieee.org/document/9733517 |
Publication date: | 16.03.2022 |
ISBN (e-book): |
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eISSN: | 2473-3628 |
DOI: | http://dx.doi.org/10.1109/mmsp53017.2021.9733517 |
Homepage: | https://ieeexplore.ieee.org/document/9733517 |
Open access |
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Authors
Vignesh Menon (internal) |
Hadi Amirpourazarian (internal) |
Christian Timmerer (internal) |
Mohammad Ghanbari (internal) |
Assignment
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
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Peer reviewed |
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