Fast Depth and Inter Mode Prediction for Quality Scalable High Efficiency Video Coding

International audienceThe scalable high efficiency video coding (SHVC) is an extension of high efficiency video coding (HEVC), which introduces multiple layers and inter-layer prediction, thus significantly increases the coding complexity on top of the already complicated HEVC encoder. In inter prediction for quality SHVC, in order to determine the best possible mode at each depth level, a coding tree unit can be recursively split into four depth levels, including merge mode, inter2Nx2N, inter2NxN, interNx2N, interNxN, in-ter2NxnU, inter2NxnD, internLx2N and internRx2N, intra modes and inter-layer reference (ILR) mode. This can obtain the highest coding efficiency, but also result in very high coding complexity. Therefore, it is crucial to improve coding speed while maintaining coding efficiency. In this research, we have proposed a new depth level and inter mode prediction algorithm for quality SHVC. First, the depth level candidates are predicted based on inter-layer correlation, spatial correlation and its correlation degree. Second, for a given depth candidate, we divide mode prediction into square and non-square mode predictions respectively. Third, in the square mode prediction, ILR and merge modes are predicted according to depth correlation, and early terminated whether residual distribution follows a Gaussian distribution. Moreover, ILR mode, merge mode and inter2Nx2N are early terminated based on significant differences in Rate Distortion (RD) costs. Fourth, if the early termination condition cannot be satisfied, non-square modes are further predicted based on significant differences in expected values of residual coefficients. Finally, inter-layer and spatial correlations are combined with residual distribution to examine whether to early terminate depth selection. Experimental results have demonstrated that, on average, the proposed algorithm can achieve a time saving of 71.14%, with a bit rate increase of 1.27%

Efficient Coding Tree Unit (CTU) Decision Method for Scalable High-Efficiency Video Coding (SHVC) Encoder

High-efficiency video coding (HEVC or H.265) is the latest video compression standard developed by the joint collaborative team on video coding (JCT-VC), finalized in 2013. HEVC can achieve an average bit rate decrease of 50% in comparison with H.264/AVC while still maintaining video quality. To upgrade the HEVC used in heterogeneous access networks, the JVT-VC has been approved scalable extension of HEVC (SHVC) in July 2014. The SHVC can achieve the highest coding efficiency but requires a very high computational complexity such that its real-time application is limited. To reduce the encoding complexity of SHVC, in this chapter, we employ the temporal-spatial and inter-layer correlations between base layer (BL) and enhancement layer (EL) to predict the best quadtree of coding tree unit (CTU) for quality SHVC. Due to exist a high correlation between layers, we utilize the coded information from the CTU quadtree in BL, including inter-layer intra/residual prediction and inter-layer motion parameter prediction, to predict the CTU quadtree in EL. Therefore, we develop an efficient CTU decision method by combing temporal-spatial searching order algorithm (TSSOA) in BL and a fast inter-layer searching algorithm (FILSA) in EL to speed up the encoding process of SHVC. The simulation results show that the proposed efficient CTU decision method can achieve an average time improving ratio (TIR) about 52–78% and 47–69% for low delay (LD) and random access (RA) configurations, respectively. It is clear that the proposed method can efficiently reduce the computational complexity of SHVC encoder with negligible loss of coding efficiency with various types of video sequences

Hybrid strategies for efficient intra prediction in spatial SHVC

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkWith multi-layer encoding and Inter-layer prediction, Spatial Scalable High Efficiency Video Coding (SSHVC) has extremely high coding complexity. It is very crucial to speed up its coding to promote widespread and cost-effective SSHVC applications. Specifically, we first reveal that the average RD cost of Inter-layer Reference (ILR) mode is different from that of Intra mode, but they both follow the Gaussian distribution. Based on this discovery, we apply the classic Gaussian Mixture Model and Expectation Maximization to determine whether ILR mode is the best mode thus skipping Intra mode. Second, when coding units (CUs) in enhancement layer use Intra mode, it indicates very simple texture is presented. We investigate their Directional Mode (DM) distribution, and divide all DMs into three classes, and then develop different methods with respect to classes to progressively predict the best DMs. Third, by jointly considering rate distortion costs, residual coefficients and neighboring CUs, we propose to employ the Conditional Random Fields model to early terminate depth selection. Experimental results demonstrate that the proposed algorithm can significantly improve coding speed with negligible coding efficiency losses

Fast Mode Assignment for Quality Scalable Extension of the High Efficiency Video Coding (HEVC) Standard: A Bayesian Approach

ABSTRACT The new compression standard, known as the High Efficiency Video Coding (HEVC), aims at significantly improving the compression efficiency compared to previous standards. There has been significant interest in developing a scalable version of this standard. As expected, the HEVC scalable video version, which is called SHVC, increases the complexity of the codec compared to the non-scalable counterpart. In this paper, we propose an adaptive fast mode assigning method based on a Bayesian classifier that reduces SHVC's coding complexity by up to 68.55%, while maintaining the overall quality and bit-rates

Multiview Video Coding for Virtual Reality

Virtual reality (VR) is one of the emerging technologies in recent years. It brings a sense of real world experience in simulated environments, hence, it is being used in many applications for example in live sporting events, music recordings and in many other interactive multimedia applications. VR makes use of multimedia content, and videos are a major part of it. VR videos are captured from multiple directions to cover the entire 360 field-of-view. It usually employs, multiple cameras of wide field-of-view such as fisheye lenses and the camera arrangement can also vary from linear to spherical set-ups. Videos in VR system are also subjected to constraints such as, variations in network bandwidth, heterogeneous mobile devices with limited decoding capacity, adaptivity for view switching in the display. The uncompressed videos from multiview cameras are redundant and impractical for storage and transmission. The existing video coding standards compresses the multiview videos effi ciently. However, VR systems place certain limitations on the video and camera arrangements, such as, it assumes rectilinear properties for video, translational motion model for prediction and the camera set-up to be linearly arranged. The aim of the thesis is to propose coding schemes which are compliant to the current video coding standards of H.264/AVC and its successor H.265/HEVC, the current state-of-the-art and multiview/scalable extensions. This thesis presents methods that compress the multiview videos which are captured from eight cameras that are arranged spherically, pointing radially outwards. The cameras produce circular fi sheye videos of 195 degree field-of-view. The final goal is to present methods, which optimize the bitrate in both storage and transmission of videos for the VR system. The presented methods can be categorized into two groups: optimizing storage bitrate and optimizing streaming bitrate of multiview videos. In the storage bitrate category, six methods were experimented. The presented methods competed against simulcast coding of individual views. The coding schemes were experimented with two data sets of 8 views each. The method of scalable coding with inter-layer prediction in all frames outperformed simulcast coding with approximately 7.9%. In the case of optimizing streaming birates, five methods were experimented. The method of scalable plus multiview skip-coding outperformed the simulcast method of coding by 36% on average. Future work will focus on pre-processing the fi sheye videos to rectilinear videos, in-order to fit them to the current translational model of the video coding standards. Moreover, the methods will be tested in comprehensive applications and system requirements

EMB: Efficient Multimedia Broadcast in Multi-tier Mobile Networks

Multimedia broadcast and multicast services (MBMS) in mobile networks has been widely addressed, however an investigation of such a technology in emerging, multi-tier, scenarios is still lacking. Notably, user clustering and resource allocation are extremely challenging in multi-tier networks, and imperative to maximize system capacity and improve quality of user-experience (QoE) in MBMS. Thus, in this paper we propose a clustering and resource allocation approach, named EMB, which specifically addresses heterogeneous networks and accounts for the fact that multimedia content is adaptively encoded into scalable layers depending on the QoE requirements and channel conditions of the heterogeneous users. Importantly, we prove that our clustering algorithm yields Pareto efficient broadcasting areas, multimedia encoding parameters, and re- source allocation, in a way that is also fair to the users. Fur- thermore, numerical results obtained under realistic conditions and using real-world video content, show that the proposed EMB results in lower churn count (i.e., higher number of served users), higher throughput, and increased QoE, while using fewer network resources

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