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

Dynamic Switching of GOP Configurations in High Efficiency Video Coding (HEVC) using Relational Databases for Multi-objective Optimization

Our current technological era is flooded with smart devices that provide significant computational resources that require optimal video communications solutions. Optimal and dynamic management of video bitrate, quality and energy needs to take into account their inter-dependencies. With emerging network generations providing higher bandwidth rates, there is also a growing need to communicate video with the best quality subject to the availability of resources such as computational power and available bandwidth. Similarly, for accommodating multiple users, there is a need to minimize bitrate requirements while sustaining video quality for reasonable encoding times. This thesis focuses on providing an efficient mechanism for deriving optimal solutions for High Efficiency Video Coding (HEVC) based on dynamic switching of GOP configurations. The approach provides a basic system for multi-objective optimization approach with constraints on power, video quality and bitrate. This is accomplished by utilizing a recently introduced framework known as Dynamically Reconfigurable Architectures for Time-varying Image Constraints (DRASTIC) in HEVC/H.265 encoder with six different GOP configurations to support optimization modes for minimum rate, maximum quality and minimum computational time (minimum energy in constant power configuration) mode of operation. Pareto-optimal GOP configurations are used in implementing the DRASTIC modes. Additionally, this thesis also presents a relational database formulation for supporting multiple devices that are characterized by different screen resolutions and computational resources. This approach is applicable to internet-based video streaming to different devices where the videos have been pre-compressed. Here, the video configuration modes are determined based on the application of database queries applied to relational databases. The database queries are used to retrieve a Pareto-optimal configuration based on real-time user requirements, device, and network constraints

Decoding-complexity-aware HEVC encoding using a complexity–rate–distortion model

The energy consumption of Consumer Electronic (CE) devices during media playback is inexorably linked to the computational complexity of decoding compressed video. Reducing a CE device's the energy consumption is therefore becoming ever more challenging with the increasing video resolutions and the complexity of the video coding algorithms. To this end, this paper proposes a framework that alters the video bit stream to reduce the decoding complexity and simultaneously limits the impact on the coding efficiency. In this context, this paper (i) first performs an analysis to determine the trade-off between the decoding complexity, video quality and bit rate with respect to a reference decoder implementation on a General Purpose Processor (GPP) architecture. Thereafter, (ii) a novel generic decoding complexity-aware video coding algorithm is proposed to generate decoding complexity-rate-distortion optimized High Efficiency Video Coding (HEVC) bit streams. The experimental results reveal that the bit streams generated by the proposed algorithm achieve 29.43% and 13.22% decoding complexity reductions for a similar video quality with minimal coding efficiency impact compared to the state-of-the-art approaches when applied to the HM16.0 and openHEVC decoder implementations, respectively. In addition, analysis of the energy consumption behavior for the same scenarios reveal up to 20% energy consumption reductions while achieving a similar video quality to that of HM 16.0 encoded HEVC bit streams

Análise do HEVC escalável : desempenho e controlo de débito

Mestrado em Engenharia Eletrónica e TelecomunicaçõesEsta dissertação apresenta um estudo da norma de codificação de vídeo de alta eficiência (HEVC) e a sua extensão para vídeo escalável, SHVC. A norma de vídeo SHVC proporciona um melhor desempenho quando codifica várias camadas em simultâneo do que quando se usa o codificador HEVC numa configuração simulcast. Ambos os codificadores de referência, tanto para a camada base como para a camada superior usam o mesmo modelo de controlo de débito, modelo R-λ, que foi otimizado para o HEVC. Nenhuma otimização de alocação de débito entre camadas foi até ao momento proposto para o modelo de testes (SHM 8) para a escalabilidade do HEVC (SHVC). Derivamos um novo modelo R-λ apropriado para a camada superior e para o caso de escalabilidade espacial, que conduziu a um ganho de BD-débito de 1,81% e de BD-PSNR de 0,025 em relação ao modelo de débito-distorção existente no SHM do SHVC. Todavia, mostrou-se também nesta dissertação que o proposto modelo de R-λ não deve ser usado na camada inferior (camada base) no SHVC e por conseguinte no HEVC.This dissertation provides a study of the High Efficiency Video Coding standard (HEVC) and its scalable extension, SHVC. The SHVC provides a better performance when encoding several layers simultaneously than using an HEVC encoder in a simulcast configuration. Both reference encoders, in the base layer and in the enhancement layer use the same rate control model, R-λ model, which was optimized for HEVC. No optimal bitrate partitioning amongst layers is proposed in scalable HEVC (SHVC) test model (SHM 8). We derived a new R-λ model for the enhancement layer and for the spatial case which led to a DB-rate gain of 1.81% and DB-PSNR gain of 0.025 in relation to the rate-distortion model of SHM-SHVC. Nevertheless, we also show in this dissertation that the proposed model of R-λ should not be used neither in the base layer nor in HEVC

Overview of the Low Complexity Enhancement Video Coding (LCEVC) Standard

The Low Complexity Enhancement Video Coding (LCEVC) specification is a recent standard approved by the ISO/IEC JTC 1/SC 29/WG04 (MPEG) Video Coding. The main goal of LCEVC is to provide a standalone toolset for the enhancement of any other existing codec. It works on top of other coding schemes, resulting in a multi-layer video coding technology, but unlike existing scalable video codecs, adds enhancement layers completely independent from the base video. The LCEVC technology takes as input the decoded video at lower resolution and adds up to two enhancement sub-layers of residuals encoded with specialized low-complexity coding tools, such as simple temporal prediction, frequency transform, quantization, and entropy encoding. This paper provides an overview of the main features of the LCEVC standard: high compression efficiency, low complexity, minimized requirements of memory and processing power

Encryption for high efficiency video coding with video adaptation capabilities

Video encryption techniques enable applications like digital rights management and video scrambling. Applying encryption on the entire video stream can be computationally costly and prevents advanced video modifications by an untrusted middlebox in the network, like splicing, quality monitoring, watermarking, and transcoding. Therefore, encryption techniques are proposed which influence a small amount of the video stream while keeping the video compliant with its compression standard, High Efficiency Video Coding. Encryption while guaranteeing standard compliance can cause degraded compression efficiency, so depending on their bitrate impact, a selection of encrypted syntax elements should be made. Each element also impacts the quality for untrusted decoders differently, so this aspect should also be considered. In this paper, multiple techniques for partial video encryption are investigated, most of them having a low impact on rate-distortion performance and having a broad range in scrambling performance(1)

Error control strategies in H.265|HEVC video transmission

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWith the rapid development in video coding technologies in the last decade, high-resolution video delivery suffers from packet loss due to unreliable transmission channels (time-varying characteristics). The error Resilience approaches at channel coding level are less efficient to implement in real time video transmission as the encoded video samples are in variable code length. Therefore, error resilience in video coding standard plays a vital role to reduce the effect of error propagation and improve the perceived visual quality. The main work in this thesis is to develop an efficient error resilience mechanism for H.265|HEVC video coding standard to reduce the effects of error propagation in error-prone conditions. In this thesis, two error resilience algorithms are proposed. The first one is Adaptive Slice Encoding (ASE) error resilience algorithm. The concept of this algorithm is to extract and protect the most active slices in the coded bitstream based on the adaptive search window. This algorithm can be applied in low delay video transmission with and without using a feedback channel. It is also designed to be compatible with reference coding software manual (HM16) for H.265|HEVC coding standard. The second proposed algorithm is a joint encoder-decoder error resilience called Error resilience based on Supplemental Enhancement Information (ERSEI) algorithm. A feedback message status is used from the decoder to notify the encoder to start encoding clean random-access picture adaptively based on the decoded picture hash message status from the decoder. At the same time, the decoder will be notified to start the error concealment process whilst waiting to receive correct video data. A recovery point message from the decoder feedback channel is used to update the encoder with error messages. In this thesis, extensive experimental work, evaluation, and comparison with state-of-the-art related algorithms have been conducted to evaluate the proposed algorithms. Furthermore, the best trade-off between the coding efficiency of the proposed error resilience algorithms and error resilience performance has been considered at the design stage. The experimental work evaluation includes both encoding conditions, i.e. error-free and error-prone. The results achieved from the experiments show significant improvements, in (Y-PSNR) results and subjective quality of the decoded bitstream, using the proposed algorithm in error-prone conditions with a variety of packet loss rates. Moreover, experimental work is conducted to test the algorithms complexity in terms of required processing execution time at both encoding and decoding stages. Additionally, the video coding standard performance for both H.264|AVC and H.265|HEVC coding standards are evaluated in error-free and error-prone environments. For ASE algorithm and when compared with improved region of interest (IROI) and region of interest (ROI) algorithms, a significant improvement in visual quality was the most obvious finding from the obtained results with PLRs of 2-18 (%). For ERSEI algorithm and when compared with the default HM16 with pixel copy concealment and motion compensated error concealment (MCEC) techniques, the evaluation results indicate clear visual quality enhancement under different packet loss rates PLRs (1,2 6, 8) %.The Ministry of Higher Education and Scientific Research in Ira