Machine Learning Approaches for Intra-Prediction in HEVC.pdf (234.11 kB)
Machine Learning Approaches for Intra-Prediction in HEVC
conference contribution
posted on 2022-04-04, 11:01 authored by Buddhiprabha Erabadda, Thanuja MallikarachchiThanuja Mallikarachchi, Gosala Kulupana, Anil FernandoThe 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
IEEEVersion
- 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.2018.8574648Print ISSN
2378-8143ISBN
978-1-5386-6309-7Cardiff Met Affiliation
- Cardiff School of Technologies
Cardiff Met Authors
Thanuja MallikarachchiCopyright Holder
- © The Publisher
Publisher Rights Statement
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