Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-prediction

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

Optimal coding unit decision for early termination in high efficiency video coding using enhanced whale optimization algorithm

Video compression is an emerging research topic in the field of block based video encoders. Due to the growth of video coding technologies, high efficiency video coding (HEVC) delivers superior coding performance. With the increased encoding complexity, the HEVC enhances the rate-distortion (RD) performance. In the video compression, the out-sized coding units (CUs) have higher encoding complexity. Therefore, the computational encoding cost and complexity remain vital concerns, which need to be considered as an optimization task. In this manuscript, an enhanced whale optimization algorithm (EWOA) is implemented to reduce the computational time and complexity of the HEVC. In the EWOA, a cosine function is incorporated with the controlling parameter A and two correlation factors are included in the WOA for controlling the position of whales and regulating the movement of search mechanism during the optimization and search processes. The bit streams in the Luma-coding tree block are selected using EWOA that defines the CU neighbors and is used in the HEVC. The results indicate that the EWOA achieves best bit rate (BR), time saving, and peak signal to noise ratio (PSNR). The EWOA showed 0.006-0.012 dB higher PSNR than the existing models in the real-time videos

Efficient HEVC-based video adaptation using transcoding

In a video transmission system, it is important to take into account the great diversity of the network/end-user constraints. On the one hand, video content is typically streamed over a network that is characterized by different bandwidth capacities. In many cases, the bandwidth is insufficient to transfer the video at its original quality. On the other hand, a single video is often played by multiple devices like PCs, laptops, and cell phones. Obviously, a single video would not satisfy their different constraints. These diversities of the network and devices capacity lead to the need for video adaptation techniques, e.g., a reduction of the bit rate or spatial resolution. Video transcoding, which modifies a property of the video without the change of the coding format, has been well-known as an efficient adaptation solution. However, this approach comes along with a high computational complexity, resulting in huge energy consumption in the network and possibly network latency. This presentation provides several optimization strategies for the transcoding process of HEVC (the latest High Efficiency Video Coding standard) video streams. First, the computational complexity of a bit rate transcoder (transrater) is reduced. We proposed several techniques to speed-up the encoder of a transrater, notably a machine-learning-based approach and a novel coding-mode evaluation strategy have been proposed. Moreover, the motion estimation process of the encoder has been optimized with the use of decision theory and the proposed fast search patterns. Second, the issues and challenges of a spatial transcoder have been solved by using machine-learning algorithms. Thanks to their great performance, the proposed techniques are expected to significantly help HEVC gain popularity in a wide range of modern multimedia applications

Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation

The ever-growing multimedia traffic has underscored the importance of effective multimedia codecs. Among them, the up-to-date lossy video coding standard, Versatile Video Coding (VVC), has been attracting attentions of video coding community. However, the gain of VVC is achieved at the cost of significant encoding complexity, which brings the need to realize fast encoder with comparable Rate Distortion (RD) performance. In this paper, we propose to optimize the VVC complexity at intra-frame prediction, with a two-stage framework of deep feature fusion and probability estimation. At the first stage, we employ the deep convolutional network to extract the spatialtemporal neighboring coding features. Then we fuse all reference features obtained by different convolutional kernels to determine an optimal intra coding depth. At the second stage, we employ a probability-based model and the spatial-temporal coherence to select the candidate partition modes within the optimal coding depth. Finally, these selected depths and partitions are executed whilst unnecessary computations are excluded. Experimental results on standard database demonstrate the superiority of proposed method, especially for High Definition (HD) and Ultra-HD (UHD) video sequences.Comment: 10 pages, 10 figure

Reducing Complexity on Coding Unit Partitioning in Video Coding: A Review

In this article, we present a survey on the low complexity video coding on a coding unit (CU) partitioning with the aim for researchers to understand the foundation of video coding and fast CU partition algorithms. Firstly, we introduce video coding technologies by explaining the trending standards and reference models. They are High Efficiency Video Coding (HEVC), Joint Exploration Test Model (JEM), and VVC, which introduce novel quadtree (QT), quadtree plus binary tree (QTBT), quadtree plus multi-type tree (QTMT) block partitioning with expensive computation complexity, respectively. Secondly, we present a comprehensive explanation of the time-consuming CU partitioning, especially for researchers who are not familiar with CU partitioning. The newer the video coding standard, the more flexible partition structures and the higher the computational complexity. Then, we provide a deep and comprehensive survey of recent and state-of-the-art researches. Finally, we include a discussion section about the advantages and disadvantage of heuristic based and learning based approaches for the readers to explore quickly the performance of the existing algorithms and their limitations. To our knowledge, it is the first comprehensive survey to provide sufficient information about fast CU partitioning on HEVC, JEM, and VVC

A Motion Estimation based Algorithm for Encoding Time Reduction in HEVC

High Efficiency Video Coding (HEVC) is a video compression standard that offers 50% more efficiency at the expense of high encoding time contrasted with the H.264 Advanced Video Coding (AVC) standard. The encoding time must be reduced to satisfy the needs of real-time applications. This paper has proposed the Multi- Level Resolution Vertical Subsampling (MLRVS) algorithm to reduce the encoding time. The vertical subsampling minimizes the number of Sum of Absolute Difference (SAD) computations during the motion estimation process. The complexity reduction algorithm is also used for fast coding the coefficients of the quantised block using a flag decision. Two distinct search patterns are suggested: New Cross Diamond Diamond (NCDD) and New Cross Diamond Hexagonal (NCDH) search patterns, which reduce the time needed to locate the motion vectors. In this paper, the MLRVS algorithm with NCDD and MLRVS algorithm with NCDH search patterns are simulated separately and analyzed. The results show that the encoding time of the encoder is decreased by 55% with MLRVS algorithm using NCDD search pattern and 56% with MLRVS using NCDH search pattern compared to HM16.5 with Test Zone (TZ) search algorithm. These results are achieved with a slight increase in bit rate and negligible deterioration in output video quality