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

Reducing the complexity of a multiview H.264/AVC and HEVC hybrid architecture

With the advent of 3D displays, an efficient encoder is required to compress the video information needed by them. Moreover, for gradual market acceptance of this new technology, it is advisable to offer backward compatibility with existing devices. Thus, a multiview H.264/Advance Video Coding (AVC) and High Efficiency Video Coding (HEVC) hybrid architecture was proposed in the standardization process of HEVC. However, it requires long encoding times due to the use of HEVC. With the aim of tackling this problem, this paper presents an algorithm that reduces the complexity of this hybrid architecture by reducing the encoding complexity of the HEVC views. By using Na < ve-Bayes classifiers, the proposed technique exploits the information gathered in the encoding of the H.264/AVC view to make decisions on the splitting of coding units in HEVC side views. Given the novelty of the proposal, the only similar work found in the literature is an unoptimized version of the algorithm presented here. Experimental results show that the proposed algorithm can achieve a good tradeoff between coding efficiency and complexity

Efficient motion estimation in H.264 reverse transcoding

Centre for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2007-2008 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Transcoding of H.264/AVC to SVC with motion data refinement

In this paper, we present motion-refined transcoding of H.264/AVC streams to SVC in the transform domain. By accurately taking into account both rate and distortion in the different layers on the one hand, and the SVC inter-layer motion prediction mechanisms on the other hand, the proposed transcoding architecture is able to improve rate-distortion performance over existing approaches. We propose a multilayer control mechanism that trades off performance between the different layers, resulting in 0.5 dB gains in the output SVC base layer

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%

Very low complexity mpeg-2 to h.264 transcoding using machine learning

ABSTRACT This paper presents a novel macroblock mode decision algorithm for inter-frame prediction based on machine learning techniques to be used as part of a very low complexity MPEG-2 to H.264 video transcoder. Since coding mode decisions take up the most resources in video transcoding, a fast macro block (MB) mode estimation would lead to reduced complexity. The proposed approach is based on the hypothesis that MB coding mode decisions in H.264 video have a correlation with the distribution of the motion compensated residual in MPEG-2 video. We use machine learning tools to exploit the correlation and derive decision trees to classify the incoming MPEG-2 MBs into one of the 11 coding modes in H.264. The proposed approach reduces the H.264 MB mode computation process into a decision tree lookup with very low complexity. The proposed transcoder is compared with a reference transcoder comprised of a MPEG-2 decoder and an H.264 encoder. Our results show that the proposed transcoder reduces the H.264 encoding time by over 95% with negligible loss in quality and bitrate

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