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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...

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Abstract-Error concealment techniques such as motion copying require significant changes to HEVC (High Efficiency Video Coding) motion estimation process when incorporated in error resilience frameworks. This paper demonstrates a novel motion estimation mechanism incorporating the concealment impact from future coding frames to achieve an average 0.73 dB gain over the state-of-the-art.

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.

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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.

Along with the rapid increase in the availability for high quality video formats such as HD (High Definition), UHD (Ultra HD) and HDR (High Dynamic Range), a huge demand for data rates during their transmission has become inevitable. Consequently, the role of video compression techniques has become crucially important in the process of mitigating the data rate requirements. Even though the latest video codec HEVC (High Efficiency Video Coding) has succeeded in significantly reducing the data rate compared to its immediate predecessor H.264/AVC (Advanced Video Coding), the HEVC coded videos in the meantime have become even more vulnerable to network impairments. Therefore, it is equally important to assess the consumers' perceived quality degradation prior to transmitting HEVC coded videos over an error prone network, and to include error resilient features so as to minimize the adverse effects those impairments. To this end, this paper proposes a probabilistic model which accurately predicts the overall distortion of the decoded video at the encoder followed by an accurate QP-λ relationship which can be used in the RDO (Rate Distortion Optimization) process. During the derivation process of the probabilistic model, the impacts from the motion vectors, the pixels in the reference frames and the clipping operations are accounted and consequently the model is capable of minimizing the prediction error as low as 3.11% whereas the state-of-theart methods can't reach below 20.08% under identical conditions. Furthermore, the enhanced RDO process has resulted in 21.41%-43.59% improvement in the BD-rate compared to the state-ofthe-art error resilient algorithms.