Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-prediction
journal contributionposted on 03.03.2022, 16:14 by Buddhiprabha Erabadda, Thanuja MallikarachchiThanuja Mallikarachchi, Chaminda Hewage, Anil Fernando
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.
Published inFuture Internet
VersionVoR (Version of Record)
CitationErabadda, B., Mallikarachchi, T., Hewage, C. and Fernando, A. (2019) 'Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-prediction', Future Internet, 11(8), 175. DOI: 10.3390/fi11080175.
Cardiff Met Affiliation
- Cardiff School of Technologies