Extreme Learning Machine-Enabled Coding Unit Partitioning Algorithm for Versatile Video Coding

The versatile video coding (VVC) standard offers improved coding efficiency compared to the high efficiency video coding (HEVC) standard in multimedia signal coding. However, this increased efficiency comes at the cost of increased coding complexity. This work proposes an efficient coding unit parti...

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Bibliographic Details
Published inInformation (Basel) Vol. 14; no. 9; p. 494
Main Authors Jiang, Xiantao, Xiang, Mo, Jin, Jiayuan, Song, Tian
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2023
Subjects
Accuracy
Algorithms
Artificial neural networks
Classification
Coding standards
coding unit
Complexity
computation complexity
Decision making
Decision trees
Distance learning
Efficiency
Entropy
extreme learning machine
Hypothesis testing
Image coding
Image quality
Machine learning
Multimedia
Neural networks
Partitioning
Statistical analysis
Support vector machines
versatile video coding
Video compression
Virtual reality
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Summary:The versatile video coding (VVC) standard offers improved coding efficiency compared to the high efficiency video coding (HEVC) standard in multimedia signal coding. However, this increased efficiency comes at the cost of increased coding complexity. This work proposes an efficient coding unit partitioning algorithm based on an extreme learning machine (ELM), which can reduce the coding complexity while ensuring coding efficiency. Firstly, the coding unit size decision is modeled as a classification problem. Secondly, an ELM classifier is trained to predict the coding unit size. In the experiment, the proposed approach is verified based on the VVC reference model. The results show that the proposed method can reduce coding complexity significantly, and good image quality can be obtained.
ISSN:2078-2489
2078-2489
DOI:10.3390/info14090494