iCUS: Intelligent CU Size Selection for HEVC Inter Prediction

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Author
Erabadda, Buddhiprabha
Mallikarachchi, Thanuja
Kulupana, Gosala
Fernando, Anil
Date
2020-08-03Acceptance date
2020-08-03
Date Deposited
2020-08-18
Type
Article
Publisher
IEEE
ISSN
2169-3536
Metadata
Show full item recordAbstract
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
Journal/conference proceeding
IEEE Access;
Citation
Erabadda, B., Mallikarachchi, T., Kulupana, G. and Fernando, A. (2020) 'iCUS: Intelligent CU Size Selection for HEVC Inter Prediction', IEEE Access, 8, pp.141143-141158. https://doi.org/10.1109/ACCESS.2020.3013804
Description
Article published in IEEE Access available open access at https://doi.org/10.1109/ACCESS.2020.3013804
Sponsorship
Cardiff Metropolitan University (Grant ID: Cardiff Metropolian (Internal))
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