# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 1 Introduction  
The recent advancements in multimedia technologies that span across content capturing, transmission, and display have made video applications ubiquitous in daily life, leading to explosive growth in demand for communication bandwidth and storage. The proliferation of mobile consumption of High Definition (HD) and Ultra High Definition (UHD) video content has made multimedia the most frequently exchanged type of content over the modern communication networks and is forecast to reach over 82% of the overall internet data traffic by 2021 [1]. However, the estimated 1.9-fold growth in network bandwidth from 2017-2022 is deemed insufficient to cater to the upcoming user demands. In addition, the requirements of video content for emerging future internet applications such as Augmented Reality (AR), Virtual Reality (VR), autonomous navigation systems, and over-the-top (OTT) multimedia consumption demand continuous improvements in video coding technologies to make these applications successful [2]. In this context, the High Efficiency Video Coding (HEVC) standard [3] introduced in 2013 provides greater compression efficiency compared to its predecessor, H.264/AVC. Experimental results of HEVC that incorporate subjective quality assessment, demonstrate ~50% improvement in its compressionefficiency [4] compared to H.264/AVC due to its vastly superior coding tools and modes. For instance, HEVC follows a hybrid block-based encoding architecture similar to that of its predecessor. However, HEVC constitutes of an assortment of novel coding features and modes including prediction modes, filtering modes, parallelisation tools, and flexible coding structures [3]. In this case, a picture is partitioned into Coding Tree Units (CTU) of size \(64\times 64\). Each CTU is further partitioned into Coding Units (CU) that can possess sizes ranging from \(8\times 8\) to \(64\times 64\). This hierarchical quadtree partitioning structure is one of the significant contributors to HEVC's improved coding efficiency performance. However, at the same time, it is also one of the major sources of encoding complexity in the HEVC coding architecture [5; 6]. The brute-force Rate-Distortion (RD) optimisation followed by the encoder to determine the best coding configuration (that includes coding structure, prediction modes, filtering options, etc.) for a given content dramatically increases the encoding complexity of HEVC. For instance, a mere increase of maximum CU size from \(16\times 16\) to \(64\times 64\) results in an average encoding time increase of 43% [4; 7]. In addition, the increased complexity of prediction modes (i.e., 35 intra-prediction modes, Advanced Motion Vector Prediction based inter-prediction) and use of 16-bit data formats in HEVC Test Model (HM) [5] result in utilising more memory bandwidth leading to high computational demand. Therefore, it is often identified that the complexity of the coding tools introduced in HEVC requires significant improvements to make the algorithms operate in real time. This is further highlighted in the recent efforts in Motion Pictures Expert Group (MPEG) to standardise approaches for low-complexity video coding [8]. In addition, recent literature has predominantly proposed algorithms to reduce the complexity of the RD optimisation that selects the optimum coding modes and structure to reduce the overall encoding complexity of HEVC. The state-of-the-art fast encoding methods for both intra- and inter-prediction can be broadly categorised into three main approaches--statistical rule-based approaches, texture properties-based approaches, and machine learning-based approaches. Having said that, focusing on the encoding complexity reduction in HEVC intra-prediction frames (predicted with minimal available information) with minimal impact on the user's perceived quality is important due to the significance of intra-predicted frames for high-quality random access encoding requirements.