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Machine Learning Approaches for Intra-Prediction in HEVC

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conference contribution
posted on 04.04.2022, 11:01 authored by Buddhiprabha Erabadda, Thanuja MallikarachchiThanuja Mallikarachchi, Gosala Kulupana, Anil Fernando
The use of machine learning techniques for encoding complexity reduction in recent video coding standards such as High Efficiency Video Coding (HEVC) has received prominent attention in the recent past. Yet, the dynamically changing nature of the video contents makes it evermore challenging to use rigid traditional inference models for predicting the encoding decisions for a given content. In this context, this paper investigates the resulting implications on the coding efficiency and the encoding complexity, when using offline trained and online trained machine-learning models for coding unit size selection in the HEVC intra-prediction. The experimental results demonstrate that the ground truth encoding statistics of the content being encoded, is crucial to the efficient encoding decision prediction when using machine learning based prediction models.

History

Presented at

2018 IEEE 7th Global Conference on Consumer Electronics (GCCE)

Published in

2018 IEEE 7th Global Conference on Consumer Electronics (GCCE)

Publisher

IEEE

Version

AM (Accepted Manuscript)

Citation

Erabadda, B., Mallikarachchi, T. , Kulupana, G. and Fernando, A. (2018) 'Machine Learning Approaches for Intra-Prediction in HEVC', 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE), Nara, 2018, pp. 206-209. doi: 10.1109/GCCE.2018.8574648

Print ISSN

2378-8143

ISBN

978-1-5386-6309-7

Cardiff Met Affiliation

  • Cardiff School of Technologies

Cardiff Met Authors

Thanuja Mallikarachchi

Copyright Holder

© The Publisher

Publisher Rights Statement

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Language

en