Machine Learning Approaches for Intra-Prediction in HEVC

Buddhiprabha Erabadda, Thanuja Mallikarachchi, Gosala Kulupana, Anil Fernando: Machine Learning Approaches for Intra-Prediction in HEVC. In: 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE), pp. 206–209, Institute of Electrical and Electronics Engineers (IEEE), 2018.

Abstract

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.

BibTeX (Download)

@inproceedings{surrey851999,
title = {Machine Learning Approaches for Intra-Prediction in HEVC},
author = {Buddhiprabha Erabadda and Thanuja Mallikarachchi and Gosala Kulupana and Anil Fernando},
url = {http://epubs.surrey.ac.uk/851999/},
doi = {10.1109/GCCE.2018.8574648},
year  = {2018},
date = {2018-12-01},
booktitle = {2018 IEEE 7th Global Conference on Consumer Electronics (GCCE)},
journal = {Proceedings of the 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE)},
pages = {206--209},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
abstract = {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.},
keywords = {Coding unit, Complexity theory, Data models, Encoding, HEVC, Intra-prediction, Machine learning, Predictive models, Support vector machines, Training, University of Surrey},
pubstate = {published},
tppubtype = {inproceedings}
}