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. INDEX TERMS Coding Unit (CU), encoder complexity reduction, High Efficiency Video Coding (HEVC), inter-prediction, Support Vector Machine (SVM) R ECENT advancements in multimedia technologies that span across Consumer Electronics (CE) in video content capturing, transmission and display have made video data the most frequently exchanged type of content over the modern communication networks. The increasing mobile consumption of High Definition (HD) and Ultra High Definition (UHD) video contents has contributed immensely towards the ever-growing IP video traffic and it is expected to reach over 82% of the overall Internet traffic in 2021 [1]. However, the estimated growth in network bandwidth (1.9 fold from 2017-2022, which is 39.0 Mbps to 75.4 Mbps for fixed broadband [1]) with time is still not sufficient to cater for the ever-growing user demands. Furthermore, the video requirements for emerging applications such as Augmented Reality (AR)/Virtual Reality (VR), interactive television, multi-party video conferences and over-the-top (OTT) multimedia consumption demand continuous improvements in the compression efficiency [2]. In this regard, High Efficiency Video Coding (HEVC) which was introduced in 2013 is the most recent stable video coding standard. It provides greater compression efficiency through an assortment of new features and coding tools over its predecessor H.264/AVC [3]. Out of these, the hierarchical quadtree partitioning structure introduced in HEVC that entails a wide range of Coding Unit (CU) sizes (i.e., 8 × 8 to 64 × 64) and their combinations, is one of the important contributors...

This paper proposes a content adaptive fast CU size selection algorithm for HEVC intra-prediction using weighted support vector machines. The proposed algorithm demonstrates an average encoding time reduction of 52.38% with 1.19% average BDBR increase compared to HM16.1 reference encoder.

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.

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.

The brute force rate-distortion optimisation based approach used in the High Efficiency Video Coding(HEVC) encoders to determine the best block partitioning structure for a given content demands an excessive amount of computational resources. In this context, this paper proposes a novel algorithm to reduce the computational complexity of HEVC inter-prediction using Support Vector Machines. The proposed algorithm predicts the Coding Unit (CU) split decision of a particular block enabling the encoder to directly encode the selected block, avoiding the unnecessary evaluation of the remaining CU size combinations. Experimental results demonstrate encoding time reductions of ~58% and ~50% with 2.27%, and 1.89% Bjøntegaard Delta Bit Rate (BDBR) losses for Random Access and Low-Delay B configurations, respectively.