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16th International Conference on Emerging Networking Experiments and Technologies

作者： Lee, Dayoung Song, Minseok Inha Univ Dept Comp Engn Incheon South Korea

ISBN: (纸本)9781450379489

transcoding is essential for live video streaming systems, but video streams in many channels may not be transcoded due to the processing capacity limitations of transcoding servers with multiple CPU nodes. To address this, we propose an algorithm that determines the transcodeable bitrate versions for each channel and allocates the transcoding tasks, by taking into account video quality, popularity, and workload balancing. Simulation results show that it can improve popularity-weighted video quality by up to 10.7% over other popularity-based alternatives.

关键词： transcoding Live Streaming Video Quality

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Assessing objective video quality in systems with multi-generation transcoding 43

Assessing objective video quality in systems with multi-gene...

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Conference on Applications of Digital Image Processing XLIII

作者： Vanam, Rahul Lillevold, Karl O. Reznik, Yuriy A. Brightcove Inc 601 Union St 1601 Seattle WA 98101 USA

ISBN: (数字)9781510638273

ISBN: (纸本)9781510638273

In a multi-generation transcoding system, the source may be an encoded mezzanine video whose objective video quality metrics (e.g., PSNR, SSIM) are unknown. transcoding process yields objective quality metrics that are relative to the encoded source video, which does not indicate the actual quality of the transcoded video relative to the original uncompressed reference video. In this paper, we present an approach for estimating the objective quality metrics of the encoded mezzanine and demonstrate that it has higher accuracy compared to a well-known scheme. Finally, we derive bounds for the end-to-end objective quality metrics of the transcoded video, and use it for controlling the transcoding process to ensure that the final transcoded video satisfies a quality criterion.

关键词： Objective video quality metrics multi-generation coding mezzanine video quality transcoding

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Transparent Vietnamese number-naming system facilitates first graders transcoding-A cross-linguistic study with French

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COGNITIVE DEVELOPMENT 2022年 61卷

作者： Mai Lien Le Noel, Marie-Pascale Catholic Univ Louvain Psychol Sci Res Inst Louvain La Neuve Belgium Vietnam Natl Univ Univ Social Sci & Humanities Fac Psychol Ho Chi Minh City Vietnam

The Vietnamese number-naming system more transparently expresses base-10 than the French one does. Our study aimed to examine the language (base-10) effect on the transcoding performance and error nature in 68 French-speaking (FS) children and 173 Vietnamese-speaking (VNS) children in the first grade. They were presented with two transcoding tasks: verbal- and analogArabic. The results showed that the VNS children performed better than the FS children on both of the transcoding tasks. The FS children committed more lexical errors than the VNS children did, while the syntactic error rates were not different. We conclude that the transparency of the base10 expressions or the reduced number lexicon led to a better transcoding performance in the VNS children, and the nature of the transcoding error reflects differences in the number-naming system. The differences in transcoding performance and errors are discussed based on the integration of the transcoding theoretical frameworks.

关键词： transcoding Cross-linguistic Base-10 Number-naming system Lexical errors Syntactic errors

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Fast VP9-to-AV1 transcoding based on Block Partitioning Inheritance

Fast VP9-to-AV1 Transcoding based on Block Partitioning Inhe...

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European Signal Processing Conference (EUSIPCO)

作者： Alex Borges Daniel Palomino Bruno Zatt Marcelo Porto Guilherme Correa Video Technology Research Group (ViTech) Federal University of Pelotas (UFPel) Brazil

ISBN: (数字)9789082797053

ISBN: (纸本)9781728150017

This paper proposes a fast VP9-to-AV1 video transcoding algorithm based on block partitioning inheritance. The proposed algorithm relies on the reuse of VP9 block partitioning during the AV1 re-encoding process. This way, the exhaustive search for the best block size option is avoided to save encoding time. The reuse of VP9 block partitioning is proposed based on a statistical analysis that shows the relation of block partitioning sizes between VP9 and AV1. The analysis demonstrates that there is a high probability of the AV1 encoding process to choose block sizes of the same size as in the VP9 encoding. Experimental results show that the proposed algorithm is able to accelerate the VP9-to-AV1 transcoding process by 28% on average at the cost of only 4% increase in the BD-Rate when compared with the complete decoding and reencoding process.

关键词： Signal processing algorithms transcoding Companies Streaming media Partitioning algorithms Decoding Acceleration

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Collaborative Framework of Cloud transcoding and Distribution Supporting Cost-Efficient Crowdsourced Live Streaming

Collaborative Framework of Cloud Transcoding and Distributio...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jiannan Zheng Haitao Zhang Yilin Jin Huadong Ma Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665408790

With the rapid development of high-speed Internet access and popularization of high-performance smart devices, past decade has witnessed the great development of crowdsourced live streaming (CLS) service. transcoding and video distribution are essential in CLS service to guarantee viewer engagement. Large CLS systems gradually migrate their services to multi-cloud platforms. However, highly dynamic viewers' requests influence transcoding and CDN distribution decisions, eventually lead to fluctuation in QoE and increase in operational cost. It is challenging for the CLS system to serve viewer's requests in multi-cloud platforms with fluctuation in cloud transcoding and distribution performance. In this paper, we propose a collaborative framework of cloud transcoding and distribution supporting CLS service. First, we define cost model and QoE model in multi-cloud platforms, comprehensively considering cloud transcoding and distribution. Second, we propose a collaborative cost-efficient approach based on multi-agent decision model. We use a G-Greedy exploration approach to learn what actions to take by exploration and exploitation based on the state of current environment. The trace-driven experiments demonstrate that our proposed approach is cost-efficient and QoE-maintained and can reduce operational cost compared with alternatives (5.37%-21.21%) while maintaining QoE of viewers.

关键词： Cloud computing Costs Fluctuations Conferences Collaboration transcoding Streaming media

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An efficient scheduling multimedia transcoding method for DASH streaming in cloud environment

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第1-Sup期22卷 1043-1053页

作者： Linh Van Ma Park, Jaehyung Nam, Jiseung Jang, Jonghyun Kim, Jinsul Chonnam Natl Univ Sch Elect & Comp Engn 77 Yongbong Ro Gwangju 500757 South Korea Elect & Telecommun Res Inst 218 Gajeong Ro Daejeon 34129 South Korea

As a result of technological evolution, streaming service providers have been dealing with the problem of delivery multimedia content to the diversity of devices with different resolutions. This issue can be solved by using dynamic adaptive streaming over hypertext (DASH) transfer protocol. However, a transcoding job in DASH requires a lot of computation resource which could lead to delaying the starting of multimedia streaming. Recently, new studies have addressed novel scheduling methods on video transcoding, but those research did not solve the problem entirely, such as the solution did not concern server performance or speed connection between a server and its requested users. Moreover, the load and speed connection status of the data servers is often unstable, leading to increasing the starting delay. So in this article, we solve such problem by modeling transcoding jobs in the form of an optimization problem and propose an algorithm to find an optimal schedule to transcode video source files. In which, we use moving average method to find average points for a short period to deal with server state changes. In the experiment, we implement our proposed method with DASH to demonstrate the effectiveness of the optimization scheduling method. In the system, we create several servers running on the Docker platform to simulate a cloud environment. Experimental results show that our methodology reduces the time of the transcoding process up to 30% compared to existing research.

关键词： Cloud computing Adaptive streaming transcoding Data replication Docker

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Efficient Intra Bitrate transcoding for Screen Content Coding Based on Convolutional Neural Network

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IEEE ACCESS 2019年 7卷 107211-107224页

作者： Kuang, Wei Chan, Yui-Lam Tsang, Sik-Ho Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China

The Screen Content Coding (SCC) extension of High Efficiency Video Coding (HEVC) is developed to improve the coding efficiency of screen content videos. To meet the diverse network requirement of different clients, bitrate transcoding for SCC is desired. This problem can be solved by a conventional brute-force transcoder (CBFT) which concatenates an original decoder and an original encoder. However, it induces high computational complexity associated with the re-encoding part of CBFT. This paper presents a convolutional neural network based bitrate transcoder (CNN-BRT) for SCC. By utilizing information from both the decoder side and the encoder side, CNN-BRT makes a fast prediction for all coding units (CUs) of a coding tree unit (CTU) in a single test. At the decoder side, decoded optimal mode maps that reflect the optimal modes and CU partitions in a CTU is derived. At the encoder side, the raw samples in a CTU are collected. Then, they are fed to CNN-BRT to make a fast prediction. To imitate the optimal mode selection in the original re-encoding part, CNN-BRT involves a loss function that takes both of the sub-optimal modes and the final optimal modes into consideration. Compared with the HEVC-SCC reference software SCM-3.0, the proposed CNN-BRT reduces encoding time by 54.86% on average with a negligible Bjontegaard delta bitrate increase of 1.01% under all-intra configuration.

关键词： transcoding screen content coding (SCC) fast algorithm convolutional neural network

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Machine learning-based H.264/AVC to HEVC transcoding via motion information reuse and coding mode similarity analysis

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IET IMAGE PROCESSING 2019年第1期13卷 34-43页

作者： Lin, Hongwei He, Xiaohai Qing, Linbo Su, Shan Xiong, Shuhua Sichuan Univ Coll Elect & Informat Engn 24 South Sect 1 Chengdu Sichuan Peoples R China Northwest Univ Nationalities Coll Elect Engn Lanzhou Gansu Peoples R China

High-efficiency video coding (HEVC), which is the latest video coding standard, is expected to have a dominant position in the market in the near future. However, most video resources are now encoded using the H.264/AVC standard. Consequently, there is a growing need for fast H.264/AVC to HEVC transcoders to facilitate the migration to the updated standard. This paper proposes a fast H.264/AVC to HEVC transcoding scheme, which constructs a three-level classifier using an optimised tree-augmented Naive Bayesian approach to predict the HEVC coding unit depth. A feature selection method is then proposed to improve prediction accuracy. A motion vector (MV) calculation method is also proposed to reduce the complexity of MV prediction in HEVC by reusing MVs from H.264/AVC. Experimental results show that, compared with other state-of-the-art transcoding algorithms, the proposed algorithm considerably reduces coding complexity while causing only negligible rate-distortion degradation.

关键词： learning (artificial intelligence) transcoding video coding code standards Bayes methods feature selection image classification trees (mathematics) computational complexity image motion analysis motion information reuse coding mode similarity analysis high-efficiency video coding video resources HEVC transcoders updated standard HEVC transcoding scheme optimised tree-augmented Naive Bayesian approach HEVC coding unit depth motion vector calculation method machine learning H 264 standard AVC standard three-level classifier feature selection method coding complexity reduction transcoding algorithms

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Joint transcoding Task Assignment and Association Control for Fog-Assisted Crowdsourced Live Streaming

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IEEE COMMUNICATIONS LETTERS 2019年第11期23卷 2036-2040页

作者： Liu, Xingchi Derakhshani, Mahsa Lambotharan, Sangarapillai Loughborough Univ Signal Proc & Networks Res Grp Wolfson Sch Mech Elect & Mfg Engn Loughborough LE11 3TU Leics England

The rapid development of content delivery networks and cloud computing has facilitated crowdsourced live-streaming platforms (CLSP) that enable people to broadcast live videos which can be watched online by a growing number of viewers. However, in order to ensure reliable viewer experience, it is important that the viewers should be provided with multiple standard video versions. To achieve this, we propose a joint fog-assisted transcoding and viewer association technique which can outsource the transcoding load to a fog device pool and determine the fog device with which each viewer will be associated, to watch desired videos. The resulting non-convex integer programming has been solved using a computationally attractive complementary geometric programming (CGP). The performance of the proposed algorithm closely matches that of the globally optimum solution obtained by an exhaustive search. Furthermore, the trace-driven simulations demonstrate that our proposed algorithm is able to provide adaptive bit rate (ABR) services.

关键词： transcoding Videos Task analysis Quality of experience Bit rate Cloud computing Standards Crowdsourced systems live video transcoding fog computing viewer association

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transcoding Resources Forecasting and Reservation for Crowdsourced Live Streaming

Transcoding Resources Forecasting and Reservation for Crowds...

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IEEE Global Communications Conference (IEEE GLOBECOM)

作者： Haouari, Fatima Baccour, Emna Erbad, Aiman Mohamed, Amr Guizani, Mohsen Qatar Univ Coll Engn CSE Dept Doha Qatar

ISBN: (纸本)9781728109626

During the last decade, empowered by the technological advances of mobile devices and the revolution of wireless mobile network access, crowdsourced live streaming has become more popular. Ensuring a stable high-quality playback experience is necessary to maximize the number of viewers and profits for content providers. Additionally, because of the instability of network conditions and the heterogeneity of the end-users capabilities, transcoding the original video into multiple bitrates is required. Video transcoding is a computationally exhaustive process, where generally a single cloud instance needs to be reserved to produce one single video bitrate representation. On-demand renting of resources or inadequate resources pre-renting may cause delay of the video playback or serving the viewers with a lower quality. On the other hand, if resources provisioning is much higher than required, the extra resources will be wasted. In this paper, we introduce our resources reservation framework for geo-distributed cloud sites, to maximize the Quality of Experience (QoE) of viewers and minimize the cost to the content providers. First, we formulate an offline optimization problem to allocate transcoding resources at the viewers' proximity, while creating a trade off between the network cost and viewers QoE. Second, based on the optimizer resource allocation decisions on historical live videos, we create our time series datasets containing historical records of the optimal resources needed at each geo-distributed cloud site. Finally, we adopt machine learning to build our distributed time series forecasting models to proactively forecast the exact needed transcoding resources ahead of time at each geo-distributed cloud site.

关键词： QoE crowdsourced live video transcoding proactive resource reservation cloud sites machine learning

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