CNN based optimal intra prediction mode estimation in video coding

Ryota Yokoyama, Masahiko Tahara, Masaru Takeuchi, Heming Sun, Yasutaka Matsuo, Jiro Katto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The amount of video data is so large that efficient video compression is required for the storage and transmission. Intra prediction is one of important components in video compression. In this paper, we examine various Convolutional Neural Network (CNN) structures to estimate optimal intra prediction mode as Most Probable Modes (MPMs). Moreover, we investigate several combinations of the MPMs obtained by the CNN and MPMs derived from High Efficiency Video Coding Test Model (HM). From these experimental results, we find that using 6 MPMs from both CNN and HM with moderate number of channels or kernel size is preferred.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Consumer Electronics, ICCE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728151861
DOIs
Publication statusPublished - 2020 Jan
Event2020 IEEE International Conference on Consumer Electronics, ICCE 2020 - Las Vegas, United States
Duration: 2020 Jan 42020 Jan 6

Publication series

NameDigest of Technical Papers - IEEE International Conference on Consumer Electronics
Volume2020-January
ISSN (Print)0747-668X

Conference

Conference2020 IEEE International Conference on Consumer Electronics, ICCE 2020
CountryUnited States
CityLas Vegas
Period20/1/420/1/6

Keywords

  • Convolutional Neural Network (CNN)
  • Intra prediction
  • Video coding

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Yokoyama, R., Tahara, M., Takeuchi, M., Sun, H., Matsuo, Y., & Katto, J. (2020). CNN based optimal intra prediction mode estimation in video coding. In 2020 IEEE International Conference on Consumer Electronics, ICCE 2020 [9043170] (Digest of Technical Papers - IEEE International Conference on Consumer Electronics; Vol. 2020-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE46568.2020.9043170