Master data

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
to publication
 ( IEEE; )
Title of the series: -
Volume number: -
First publication: Yes
Version: -
Page: pp. 1 - 6

Versionen

Keine Version vorhanden
Publication date: 06.10.2021
ISBN:
  • 978-1-6654-3287-0
ISSN: 2163-3517
Homepage: https://ieeexplore.ieee.org/document/9733517
Publication date: 16.03.2022
ISBN (e-book):
  • 978-1-6654-3288-7
eISSN: 2473-3628
DOI: http://dx.doi.org/10.1109/mmsp53017.2021.9733517
Homepage: https://ieeexplore.ieee.org/document/9733517
Open access
  • Available online (not open access)

Assignment

Organisation Address
Fakultät für Technische Wissenschaften
 
Institut für Informationstechnologie
Universitaetsstr. 65-67
9020 Klagenfurt am Wörthersee
Austria
   martina.steinbacher@aau.at
http://itec.aau.at/
To organisation
Universitaetsstr. 65-67
AT - 9020  Klagenfurt am Wörthersee

Categorisation

Subject areas
  • 1020 - Computer Sciences
Research Cluster No research Research Cluster selected
Peer reviewed
  • Yes
Publication focus
  • Science to Science (Quality indicator: II)
Classification raster of the assigned organisational units:
working groups
  • Multimedia Communication

Cooperations

No partner organisations selected

Articles of the publication

No related publications