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9th IEEE/EMBS International Conference on Neural Engineering (NER)

作者： Liu, Xueqing Sajda, Paul Columbia Univ Dept Biomed Engn New York NY 10027 USA Columbia Univ Dept Elect Engn & Radiol Phys New York NY 10027 USA Columbia Univ Data Sci Inst New York NY 10027 USA

ISBN: (纸本)9781538679210

In this paper, we use convolutional neural networks (CNNs) to model/capture the relationship between simultaneously acquired EEG and fMRI. Specifically we use CNNs to implement "neural transcoding" - i.e. generating one neuroimaging modality from another -from EEG to fMRI and vice versa. The novelty of our approach lies in its ability to resolve the source space without prior hemodynamic and leadfield estimation. The two CNNs, one for EEG-to-fMRI and the other fMRI-to-EEG transcoding, are coupled in their source space representations, and given their architecture are able to capture both linear and non-linear transformations that map two imaging modalities into a common neural source space. We present results on simulated simultaneously acquired EEG-fMRI data and show the performance of mapping each modality to the other, the accuracy of recovering source space, and the effects of noise and variation in the simulated acquisition parameters, such as MRI slice timing, on the results.

关键词： Brain modeling Electroencephalography Functional magnetic resonance imaging Data models Convolution transcoding Correlation

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FAST HEVC-TO-AV1 transcoding BASED ON CODING UNIT DEPTH INHERITANCE 26

FAST HEVC-TO-AV1 TRANSCODING BASED ON CODING UNIT DEPTH INHE...

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26th IEEE International Conference on Image Processing (ICIP)

作者： Borges, Alex Zatt, Bruno Porto, Marcelo Correa, Guilherme Univ Fed Pelotas Comp Sci Grad Program Video Technol Res Grp ViTech Pelotas RS Brazil

ISBN: (纸本)9781538662496

With the advent of the recently launched AOMedia Video 1 (AV1) bitstream specification, there is currently a need for converting legacy content encoded with the state-of-the-art High Efficiency Video Coding (HEVC) standard to the new format. However, transcoding is a complex task composed of a decoding and an encoding process in sequence, which requires long processing times and high energy consumption. This paper proposes the first HEVC-to-AV1 transcoding solution, which is based on the high correlation between block size decisions in HEVC and AV1. The solution allows the AV1 encoder to inherit Coding Unit (CU) depth information from the HEVC bitstream to constrain the AV1 re-encoding process. Experimental results show an average transcoding time reduction of 35.41% at the cost of a compression efficiency loss of 4.54%.

关键词： transcoding HEVC AV1 video coding complexity reductiond

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A Fast DVC to HEVC transcoding for Mobile Video Communication 3

A Fast DVC to HEVC Transcoding for Mobile Video Communicatio...

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IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Yang, Jia Qing, Linbo He, Xiaohai Hua, Ling Rong, Song Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Sichuan Peoples R China Chengdu Univ Informat Technol Coll Commun Engn Chengdu 610225 Sichuan Peoples R China

ISBN: (纸本)9781538662434

In the geological hazards rescue, it is one of the most important demands to provide the longer real-time video communication with the same battery capacity. In this paper, a novel fast transcoding from distributed video coding (DVC) to high efficiency video coding (HEVC) is proposed to provide an effective solution. Firstly, the proposed transcoding shifts the high computational complexity including DVC decoding and HEVC encoding to the DVC-HEVC transcoding server. And then, by means of support vector machine, this algorithm fully exploits the correlation between DVC and HEVC to accelerate the transcoding. The experimental results show that compared with the traditional cascade transcoding, the proposed algorithm can save the transcoding time complexity, on average, by 52.44% with acceptable RD performance loss.

关键词： transcoding mobile video communication emergency communication

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CPU Microarchitectural Performance Characterization of Cloud Video transcoding

CPU Microarchitectural Performance Characterization of Cloud...

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IEEE International Workshop/Symposium on Workload Characterization

作者： Yuhan Chen Jingyuan Zhu Tanvir Ahmed Khan Baris Kasikci University of Michigan

ISBN: (数字)9781728176451

ISBN: (纸本)9781728176468

Video streaming accounts for more than 75% of all Internet traffic. Videos streamed to end-users are encoded to reduce their size in order to efficiently use the Internet traffic, and are decoded when played at end-users' devices. Videos have to be transcoded-i.e., where one encoding format is converted to another-to fit users' different needs of resolution, framerate and encoding format. Global streaming service providers (e.g., YouTube, Netflix, and Facebook) employ a large number of transcoding operations. Optimizing the performance of transcoding to provide speedup of a few percent can save millions of dollars in computational and energy costs. While prior works identified microarchitectural characteristics of the transcoding operation for different classes of videos, other parameters of video transcoding and their impact on CPU performance has yet to be studied. In this work, we investigate the microarchitectural performance of video transcoding with all videos from vbench, a publicly available cloud video benchmark suite. We profile the leading multimedia transcoding software, FFmpeg with all of its major configurable parameters across videos with different complexity (e.g., videos with high motion and frequent scene transition are more complex). Based on our profiling results, we find key bottlenecks in instruction cache, data cache, and branch prediction unit for video transcoding workloads. Moreover, we observe that these bottlenecks vary widely in response to variation in transcoding parameters. We leverage several state-of-the-art compiler approaches to mitigate performance bottlenecks of video transcoding operations. We apply AutoFDO, a feedback-directed optimization (FDO) tool to improve instruction cache and branch prediction performance. To improve data cache performance, we leverage Graphite, a polyhedral optimizer. Across all videos, AutoFDO and Graphite provide average speedups of 4.66% and 4.42% respectively. We also set up simulation settings wit

关键词： Streaming media transcoding Microarchitecture Bit rate Complexity theory Video recording Quality assessment

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ComplexCTTP: Complexity Class Based transcoding Time Prediction for Video Sequences Using Artificial Neural Network

ComplexCTTP: Complexity Class Based Transcoding Time Predict...

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IEEE International Conference on Multimedia Big Data (BigMM)

作者： Anatoliy Zabrovskiy Prateek Agrawal Roland Mathá Christian Timmerer Radu Prodan University of Klagenfurt Klagenfurt Austria Lovely Professional University Punjab India Bitmovin Klagenfurt Austria

ISBN: (数字)9781728193250

ISBN: (纸本)9781728193267

HTTP Adaptive Streaming of video content is becoming an integral part of the Internet and accounts for the majority of today's traffic. Although Internet bandwidth is constantly increasing, video compression technology plays an important role and the major challenge is to select and set up multiple video codecs, each with hundreds of transcoding parameters. Additionally, the transcoding speed depends directly on the selected transcoding parameters and the infrastructure used. Predicting transcoding time for multiple transcoding parameters with different codecs and processing units is a challenging task, as it depends on many factors. This paper provides a novel and considerably fast method for transcoding time prediction using video content classification and neural network prediction. Our artificial neural network (ANN) model predicts the transcoding times of video segments for state of the art video codecs based on transcoding parameters and content complexity. We evaluated our method for two video codecs/implementations (AVC/x264 and HEVC/x265) as part of large-scale HTTP Adaptive Streaming services. The ANN model of our method is able to predict the transcoding time by minimizing the mean absolute error (MAE) to 1.37 and 2.67 for x264 and x265 codecs, respectively. For x264, this is an improvement of 22% compared to the state of the art.

关键词： transcoding Streaming media Complexity theory Predictive models Task analysis Bit rate Video sequences

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Custom Silicon at Facebook: A Datacenter Infrastructure Perspective on Video transcoding and Machine Learning

Custom Silicon at Facebook: A Datacenter Infrastructure Pers...

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International Electron Devices Meeting (IEDM)

作者： Prahlad Venkatapuram Zhao Wang Chandra Mallipedi Facebook Menlo Park CA USA

ISBN: (数字)9781728188881

ISBN: (纸本)9781728188898

This paper describes several important aspects of the Application Specific Integrated Circuits (ASIC) development efforts happening at Facebook to serve its hyperscale datacenter networks. Motivations, key features, benefits and challenges for custom silicon development supporting machine learning algorithms and video processing are introduced. Power strategies and reliability considerations are also addressed.

关键词： Application specific integrated circuits Machine learning algorithms Social networking (online) transcoding Machine learning Silicon Integrated circuit reliability

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Efficient Inter-Prediction transcoding Algorithm from H.264/AVC to HEVC 2019

Efficient Inter-Prediction Transcoding Algorithm from H.264/...

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Proceedings of the 2019 The 2nd International Conference on Robotics, Control and Automation Engineering

作者： Xiangqun Li Jing Zhao Mingliang Gao Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education and College of electrical engineering Northwest Minzu University Lanzhou Gansu China The hospital Northwest Minzu University Lanzhou China Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education and College of electrical engineering Northwest Minzu University Lanzhou China

ISBN: (纸本)9781450376228

With the development of the High Efficiency Video Coding (HEVC) video compression standard, there is an urgent need for more research on video format transcoding from H.264/Advanced Video Coding (AVC) to HEVC. In this paper, we highlight the difference between the inter-prediction mechanisms of the two standards, and propose detailed steps to complete bit stream transcoding from H.264/AVC to HEVC. In the transcoding process, we use information extracted from the H.264/AVC decoder in conjunction with our proposed algorithm, called "merging smaller blocks into a larger one" (MSBLO), to accurately obtain information regarding the coding unit (CU) size and the motion vector (MV), along with the prediction mode of the prediction unit (PU). This ultimately reduces the computational complexity of the determination of the optimum inter-prediction mode in the transcoding process. Experimental results show that our proposed transcoding algorithm reduces the computational complexity of the current HEVC model by approximately 90% with a lower rate distortion loss of coding efficiency.

关键词： video coding HEVC inter-prediction transcoding H.264/AVC

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Allies: Tile-Based Joint transcoding, Delivery and Caching of 360° Videos in Edge Cloud Networks

Allies: Tile-Based Joint Transcoding, Delivery and Caching o...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Jianxin Shi Lingjun Pu Jingdong Xu Tianjin Key Laboratory of Network and Data Security Technology Tianjin China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

360° or panoramic video applications have seen booming development and absorbed great attention in recent years. However, as they are of significant size and usually watched from a close distance, they require an extremely higher bandwidth and frame rate for a good immersible experience, which poses a great challenge on mobile networks. Realizing the great potentials of tile-based transcoding, viewport adaptive streaming, and edge caching, we propose Allies, a tile-based joint transcoding, delivery, and caching framework for 360° video services in edge cloud networks. Meanwhile, an innovative idea about 360° video caching way is proposed and applied to improve the cache utilization of edge clouds. In this framework, we formulate the joint optimization problem as an integer nonlinear program and propose a greedy suboptimal algorithm with polynomial running time to minimize video service costs. Finally, extensive simulations with real user's head movement traces and corresponding 360° video datasets corroborate the efficiency, flexibility, and lightweight of our proposed algorithm; for instance, it achieves over 25% performance improvement compared to state-of-the-art works in various system settings.

关键词： Cloud computing Conferences Collaboration transcoding Bandwidth Streaming media Optimization

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ENERGY EFFICIENT MOBILE WEB VIA SCRIPTS&STYLESHEETS BASED transcoding

ENERGY EFFICIENT MOBILE WEB VIA SCRIPTS&STYLESHEETS BASED TR...

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作者： HUSEYIN UNLU Middle East Technical University

学位级别：硕士

Mobile devices have become essential in our daily lives and the requirements of the mobile platform are increasing everyday. Although mobile devices nowadays are technically much more stronger than the desktops in the past and their battery capacity is increasing at around 3% per year, they still have some limitations in terms of battery size, processing power and device memory. These limitations have effects on browsing web pages since they are not totally designed for mobile use and it takes more power than necessary on the client side. In order to save energy and extend the battery life, there are some guidelines for web site programmers. However, most programmers are not aware of these guidelines and therefore most web sites do not adhere to these guidelines. Important components of modern web sites are the scripts that make them dynamic and stylesheets that are used for visual rendering. These two are external components of web sites that are shown to have effect on the downloading time of web pages. This MSc thesis first investigates the effect of scripts and stylesheets on the energy consumption of web pages on mobile devices, and then propose two techniques, which are (1) concatenating external script and stylesheet files and (2) minifying external script and stylesheets, that can be used to transcode web pages to improve energy consumption and therefore improve battery life on the client side, without changing the look&feel of the web pages and without adding extra load on the client side or the server. The evaluation results show that the proposed techniques achieved statistically significant energy saving.

关键词： transcoding Energy Saving Browsing Mobile Web Web Engineering

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A fast intra H.264/AVC to HEVC transcoding system

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第5期77卷 6367-6384页

作者： Diaz-Honrubia, A. J. Martinez, J. L. Cuenca, P. Univ Castilla La Mancha High Performance Networks & Architectures Lab Albacete Spain

The H.264/Advanced Video Coding (AVC) standard has been widely used in the last 10 years, and this has led to a large amount of legacy video being currently encoded in this format. However, the new High Efficiency Video Coding (HEVC) standard has recently been developed and it is thought that it will dominate the market in the next few years. These two facts make an efficient transcoding system between H.264/AVC and HEVC necessary. Moreover, it should be taken into account that intra sequences are commonly used in certain scenarios, such as video editing and post-production, making a migration of these intra contents from H.264/AVC to HEVC necessary. This paper proposes a fast intra H.264/AVC to HEVC transcoding system that is based on Bayesian classifiers and which outperforms other state-of-the-art algorithms, achieving a good complexity reduction with a negligible bit rate increase.

关键词： HEVC H.264/AVC transcoding CTU splitting Intra prediction

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