Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction

Buddhiprabha Erabadda, Thanuja Mallikarachchi, Chaminda Hewage and Anil Fernando
Additional contact information
Buddhiprabha Erabadda: Centre for Vision Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK
Thanuja Mallikarachchi: Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
Chaminda Hewage: Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
Anil Fernando: Centre for Vision Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK

Future Internet, 2019, vol. 11, issue 8, 1-18

Abstract: The exorbitant increase in the computational complexity of modern video coding standards, such as High Efficiency Video Coding (HEVC), is a compelling challenge for resource-constrained consumer electronic devices. For instance, the brute force evaluation of all possible combinations of available coding modes and quadtree-based coding structure in HEVC to determine the optimum set of coding parameters for a given content demand a substantial amount of computational and energy resources. Thus, the resource requirements for real time operation of HEVC has become a contributing factor towards the Quality of Experience (QoE) of the end users of emerging multimedia and future internet applications. In this context, this paper proposes a content-adaptive Coding Unit (CU) size selection algorithm for HEVC intra-prediction. The proposed algorithm builds content-specific weighted Support Vector Machine (SVM) models in real time during the encoding process, to provide an early estimate of CU size for a given content, avoiding the brute force evaluation of all possible coding mode combinations in HEVC. The experimental results demonstrate an average encoding time reduction of 52.38%, with an average Bjøntegaard Delta Bit Rate (BDBR) increase of 1.19% compared to the HM16.1 reference encoder. Furthermore, the perceptual visual quality assessments conducted through Video Quality Metric (VQM) show minimal visual quality impact on the reconstructed videos of the proposed algorithm compared to state-of-the-art approaches.

Keywords: HEVC; video coding; encoding complexity; Support Vector Machines; intra-prediction; Quality of Experience; VQM (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/11/8/175/pdf (application/pdf)
https://www.mdpi.com/1999-5903/11/8/175/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:11:y:2019:i:8:p:175-:d:256735

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().