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.

Highlights

  • 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

  • This section first describes the hierarchical quadtree partitioning structure employed in the High Efficiency Video Coding (HEVC) encoding architecture followed by an elaboration of the state-of-the-art methods available in the literature that focus on reducing the encoding complexity

  • These include HM16.1 [15], Support Vector Machine (SVM)-based Coding Units (CU) size selection algorithm proposed by Zhang et al [10], and fast CU selection algorithms proposed by Liu et al [34]

Read more

Summary

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 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. A typical encoder in this case follows a brute-force approach of evaluating all possible coding parameter combinations using RD optimisation to determine the optimum set of coding parameter combination for a given content

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call