iCUS: Intelligent CU Size Selection for HEVC Inter Prediction

Erabadda, Buddhiprabha, Mallikarachchi, Thanuja, Kulupana, Gosala and Fernando, Anil (2020) iCUS: Intelligent CU Size Selection for HEVC Inter Prediction IEEE Access, 8. pp. 141143-141158.

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Abstract

The hierarchical quadtree partitioning of Coding Tree Units (CTU) is one of the striking features in HEVC that contributes towards its superior coding performance over its predecessors. However, the brute force evaluation of the quadtree hierarchy using the Rate-Distortion (RD) optimisation, to determine the best partitioning structure for a given content, makes it one of the most time-consuming operations in HEVC encoding. In this context, this paper proposes an intelligent fast Coding Unit (CU) size selection algorithm to expedite the encoding process of HEVC inter-prediction. The proposed algorithm introduces (i) two CU split likelihood modelling and classification approaches using Support Vector Machines (SVM) and Bayesian probabilistic models, and (ii) a fast CU selection algorithm that makes use of both offline trained SVMs and online trained Bayesian probabilistic models. Finally, (iii) a computational complexity to coding efficiency trade-off mechanism is introduced to flexibly control the algorithm to suit different encoding requirements. The experimental results of the proposed algorithm demonstrate an average encoding time reduction performance of 53.46%, 61.15%, and 58.15% for Low Delay B , Random Access , and Low Delay P configurations, respectively, with Bjøntegaard Delta-Bit Rate (BD-BR) losses of 2.35%, 2.9%, and 2.35%, respectively, when evaluated across a wide range of content types and quality levels.

Item Type:	Article
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :	Name Email ORCID Erabadda, Buddhiprabha Mallikarachchi, Thanuja Kulupana, Gosala Fernando, Anil W.Fernando@surrey.ac.uk
Date :	3 August 2020
DOI :	10.1109/ACCESS.2020.3013804
Copyright Disclaimer :	© 2020 IEEE. . This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Depositing User :	Clive Harris
Date Deposited :	02 Oct 2020 13:41
Last Modified :	02 Oct 2020 13:41
URI:	http://epubs.surrey.ac.uk/id/eprint/858637

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