Fast intra prediction mode decision for HEVC using random forest

Zhuge Yan, Siu Yeung Cho, Sherif Welsen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)
16 Downloads (Pure)

Abstract

In this paper, we extracted specific image features that represent CU texture, incorporate a machine learning technique, namely random forest, in HEVC intra prediction mode selection, to improve the performance of intra coding of HEVC. Compared with similar algorithms, our method extracts very specific features of image texture changes in terms of angle. Therefore the proposed method can achieve very high prediction accuracy. Having similar reduction in complexity, the proposed algorithm can gain higher video quality compared with similar algorithms. 2019 Copyright is held by the owner/author(s).

Original languageEnglish
Title of host publicationACM International Conference Proceeding Series
PublisherAssociation for Computing Machinery
Pages45-49
Number of pages5
ISBN (Print)9781450361750
DOIs
Publication statusPublished - 2019
Event2019 International Conference on Image, Video and Signal Processing, IVSP 2019 - Shanghai, China
Duration: 25 Feb 201928 Feb 2019

Publication series

NameACM International Conference Proceeding Series
VolumePart F147767

Conference

Conference2019 International Conference on Image, Video and Signal Processing, IVSP 2019
Country/TerritoryChina
CityShanghai
Period25/02/1928/02/19

Keywords

  • HEVC
  • Intra prediction
  • Random forest

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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