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

Algorithms & implementation of advanced video coding standards

Advanced video coding standards have become widely deployed coding techniques used in numerous products, such as broadcast, video conference, mobile television and blu-ray disc, etc. New compression techniques are gradually included in video coding standards so that a 50% compression rate reduction is achievable every five years. However, the trend also has brought many problems, such as, dramatically increased computational complexity, co-existing multiple standards and gradually increased development time. To solve the above problems, this thesis intends to investigate efficient algorithms for the latest video coding standard, H.264/AVC. Two aspects of H.264/AVC standard are inspected in this thesis: (1) Speeding up intra4x4 prediction with parallel architecture. (2) Applying an efficient rate control algorithm based on deviation measure to intra frame. Another aim of this thesis is to work on low-complexity algorithms for MPEG-2 to H.264/AVC transcoder. Three main mapping algorithms and a computational complexity reduction algorithm are focused by this thesis: motion vector mapping, block mapping, field-frame mapping and efficient modes ranking algorithms. Finally, a new video coding framework methodology to reduce development time is examined. This thesis explores the implementation of MPEG-4 simple profile with the RVC framework. A key technique of automatically generating variable length decoder table is solved in this thesis. Moreover, another important video coding standard, DV/DVCPRO, is further modeled by RVC framework. Consequently, besides the available MPEG-4 simple profile and China audio/video standard, a new member is therefore added into the RVC framework family. A part of the research work presented in this thesis is targeted algorithms and implementation of video coding standards. In the wide topic, three main problems are investigated. The results show that the methodologies presented in this thesis are efficient and encourage

Scalable and network aware video coding for advanced communications over heterogeneous networks

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityThis work addresses the issues concerned with the provision of scalable video services over heterogeneous networks particularly with regards to dynamic adaptation and user’s acceptable quality of service. In order to provide and sustain an adaptive and network friendly multimedia communication service, a suite of techniques that achieved automatic scalability and adaptation are developed. These techniques are evaluated objectively and subjectively to assess the Quality of Service (QoS) provided to diverse users with variable constraints and dynamic resources. The research ensured the consideration of various levels of user acceptable QoS The techniques are further evaluated with view to establish their performance against state of the art scalable and non-scalable techniques. To further improve the adaptability of the designed techniques, several experiments and real time simulations are conducted with the aim of determining the optimum performance with various coding parameters and scenarios. The coding parameters and scenarios are evaluated and analyzed to determine their performance using various types of video content and formats. Several algorithms are developed to provide a dynamic adaptation of coding tools and parameters to specific video content type, format and bandwidth of transmission. Due to the nature of heterogeneous networks where channel conditions, terminals, users capabilities and preferences etc are unpredictably changing, hence limiting the adaptability of a specific technique adopted, a Dynamic Scalability Decision Making Algorithm (SADMA) is developed. The algorithm autonomously selects one of the designed scalability techniques basing its decision on the monitored and reported channel conditions. Experiments were conducted using a purpose-built heterogeneous network simulator and the network-aware selection of the scalability techniques is based on real time simulation results. A technique with a minimum delay, low bit-rate, low frame rate and low quality is adopted as a reactive measure to a predicted bad channel condition. If the use of the techniques is not favoured due to deteriorating channel conditions reported, a reduced layered stream or base layer is used. If the network status does not allow the use of the base layer, then the stream uses parameter identifiers with high efficiency to improve the scalability and adaptation of the video service. To further improve the flexibility and efficiency of the algorithm, a dynamic de-blocking filter and lambda value selection are analyzed and introduced in the algorithm. Various methods, interfaces and algorithms are defined for transcoding from one technique to another and extracting sub-streams when the network conditions do not allow for the transmission of the entire bit-stream

Distributed Coding/Decoding Complexity in Video Sensor Networks

Video Sensor Networks (VSNs) are recent communication infrastructures used to capture and transmit dense visual information from an application context. In such large scale environments which include video coding, transmission and display/storage, there are several open problems to overcome in practical implementations. This paper addresses the most relevant challenges posed by VSNs, namely stringent bandwidth usage and processing time/power constraints. In particular, the paper proposes a novel VSN architecture where large sets of visual sensors with embedded processors are used for compression and transmission of coded streams to gateways, which in turn transrate the incoming streams and adapt them to the variable complexity requirements of both the sensor encoders and end-user decoder terminals. Such gateways provide real-time transcoding functionalities for bandwidth adaptation and coding/decoding complexity distribution by transferring the most complex video encoding/decoding tasks to the transcoding gateway at the expense of a limited increase in bit rate. Then, a method to reduce the decoding complexity, suitable for system-on-chip implementation, is proposed to operate at the transcoding gateway whenever decoders with constrained resources are targeted. The results show that the proposed method achieves good performance and its inclusion into the VSN infrastructure provides an additional level of complexity control functionality

Efficient bit rate transcoding for high efficiency video coding

High efficiency video coding (HEVC) shows a significant advance in compression efficiency and is considered to be the successor of H.264/AVC. To incorporate the HEVC standard into real-life network applications and a diversity of other applications, efficient bit rate adaptation (transrating) algorithms are required. A current problem of transrating for HEVC is the high computational complexity associated with the encoder part of such a cascaded pixel domain transcoder. This paper focuses on deriving an optimal strategy for reducing the transcoding complexity with a complexity-scalable scheme. We propose different transcoding techniques which are able to reduce the transcoding complexity in both CU and PU optimization levels. At the CU level, CUs can be evaluated in top-to-bottom or bottom-to-top flows, in which the coding information of the input video stream is utilized to reduce the number of evaluations or to early terminate certain evaluations. At the PU level, the PU candidates are adaptively selected based on the probability of PU sizes and the co-located input PU partitioning. Moreover, with the use of different proposed methods, a complexity-scalable transrating scheme can be achieved. Furthermore, the transcoding complexity can be effectively controlled by the machine learning based approach. Simulations show that the proposed techniques provide a superior transcoding performance compared to the state-of-the-art related works. Additionally, the proposed methods can achieve a range of trade-offs between transrating complexity and coding performance. From the proposed schemes, the fastest approach is able to reduce the complexity by 82% while keeping the bitrate loss below 3%

Algorithms and methods for video transcoding.

Video transcoding is the process of dynamic video adaptation. Dynamic video adaptation can be defined as the process of converting video from one format to another, changing the bit rate, frame rate or resolution of the encoded video, which is mainly necessitated by the end user requirements. H.264 has been the predominantly used video compression standard for the last 15 years. HEVC (High Efficiency Video Coding) is the latest video compression standard finalised in 2013, which is an improvement over H.264 video compression standard. HEVC performs significantly better than H.264 in terms of the Rate-Distortion performance. As H.264 has been widely used in the last decade, a large amount of video content exists in H.264 format. There is a need to convert H.264 video content to HEVC format to achieve better Rate-Distortion performance and to support legacy video formats on newer devices. However, the computational complexity of HEVC encoder is 2-10 times higher than that of H.264 encoder. This makes it necessary to develop low complexity video transcoding algorithms to transcode from H.264 to HEVC format. This research work proposes low complexity algorithms for H.264 to HEVC video transcoding. The proposed algorithms reduce the computational complexity of H.264 to HEVC video transcoding significantly, with negligible loss in Rate-Distortion performance. This work proposes three different video transcoding algorithms. The MV-based mode merge algorithm uses the block mode and MV variances to estimate the split/non-split decision as part of the HEVC block prediction process. The conditional probability-based mode mapping algorithm models HEVC blocks of sizes 16×16 and lower as a function of H.264 block modes, H.264 and HEVC Quantisation Parameters (QP). The motion-compensated MB residual-based mode mapping algorithm makes the split/non-split decision based on content-adaptive classification models. With a combination of the proposed set of algorithms, the computational complexity of the HEVC encoder is reduced by around 60%, with negligible loss in Rate-Distortion performance, outperforming existing state-of-art algorithms by 20-25% in terms of computational complexity. The proposed algorithms can be used in computation-constrained video transcoding applications, to support video format conversion in smart devices, migration of large-scale H.264 video content from host servers to HEVC, cloud computing-based transcoding applications, and also to support high quality videos over bandwidth-constrained networks