检索结果-内蒙古大学图书馆

IEEE International Conference on Image Processing (ICIP)

作者： Hanooman, Varoun Kokaram, Anil C. Su, Yeping Birkbeck, Neil Adsumili, Balu Trinity Coll Dublin Elect & Elect Dept Dublin Ireland Google YouTube Mountain View CA USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

The volume of User Generated Content (UGC) on the internet has exploded throughout the pandemic. The relatively low quality of that content generally implies an increased bitrate and much reduced quality after transcoding. Preprocessing e.g. using a noise reducer, is one approach for reducing bitrate and increasing quality. The impact of the noise reducer is however affected by the target bitrate of the encoder. That relationship is known but not previously quantitatively examined. In this paper we present a methodology and new metric for measuring this impact based on the Rate-Distortion curves before and after pre-processing. The metric is used as a cost function for estimating the optimal filter parameter for our chosen denoiser. Our experiments show that optimising the filter parameter in this way yields as much as 4-5dB improvement in PSNR at 3 Mbps.

关键词： preprocessor denoising codec transcoding compression

来源：评论

学校读者我要写书评

暂无评论

An empirical approach for estimating the effect of a transcoding aware preprocessor 45

An empirical approach for estimating the effect of a transco...

引用

Applications of Digital Image Processing XLV

作者： Hanooman, Varoun Su, Yeping Birkbeck, Neil Adsumilli, Balu Kokaram, Anil Trinity Coll Dublin Elect Engn Dept Sigmedia Grp Dublin Ireland YouTube Google Media Algorithms Team Mountain View CA USA

ISBN: (纸本)9781510654372;9781510654365

Video compression is complicated by degradation in User Generated Content (UGC). Preprocessing the data before encoding improves compression. However the impact of the preprocessor depends not only on the codec and the filter strength of the preprocessor being used but also on the target bitrate of the encode and the level of degradation. In this paper we present a framework for modelling this relationship and estimating the optimal filter strength for a particular codec/preprocessor/bitrate/degradation combination. We examine two preprocessors based on classical and DNN ideas, and two codecs AV1, VP9. We find that up to 2dB of quality gain can result from preprocessing at constant bitrate and our estimator is accurate enough to capture most of these gains.

关键词： Preprocessing video streaming transcoding compression AV1 VP9 denoising

来源：评论

学校读者我要写书评

暂无评论

Optimal Wireless Streaming of Multi-Quality 360 VR Video By Exploiting Natural, Relative Smoothness-Enabled, and transcoding-Enabled Multicast Opportunities

引用

IEEE TRANSACTIONS ON MULTIMEDIA 2021年 23卷 3670-3683页

作者： Long, Kaixuan Cui, Ying Ye, Chencheng Liu, Zhi Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Elect Commun Tokyo Japan

In this paper, we would like to investigate the optimal wireless streaming of a multi-quality tiled 360 virtual reality (VR) video from a server to multiple users. To this end, we propose to maximally exploit potential multicast opportunities by effectively utilizing characteristics of multi-quality tiled 360 VR videos and computation resources at the users' side. In particular, we consider two requirements for quality variation in one field-of-view (FoV), i.e., the absolute smoothness requirement and the relative smoothness requirement, and two video playback modes, i.e., the direct-playback mode (without user transcoding) and transcode-playback mode (with user transcoding). Besides natural multicast opportunities, we introduce two new types of multicast opportunities, namely, relative smoothness-enabled multicast opportunities, which allow a flexible tradeoff between viewing quality and communications resource consumption, and transcoding-enabled multicast opportunities, which allow a flexible tradeoff between computation and communications resource consumptions. Then, we establish a novel mathematical model that reflects the impacts of natural, relative smoothness-enabled, and transcoding-enabled multicast opportunities on the average transmission energy and transcoding energy. Based on this model, we optimize the transmission resource allocation, playback quality level selection, and transmission quality level selection to minimize the energy consumption in the four cases with different requirements for quality variation and video playback modes. By comparing the optimal values in the four cases, we prove that the energy consumption reduces when more multicast opportunities can be utilized. Finally, numerical results show substantial gains of the proposed solutions over existing schemes, and demonstrate the importance of exploiting of the three types of multicast opportunities.

关键词： Streaming media Wireless communication transcoding Optimization Resource management Energy consumption Head Wireless streaming virtual reality 360 video multi-quality multicast smoothness transcoding convex optimization DC programming

来源：评论

学校读者我要写书评

暂无评论

OTEC: An Optimized transcoding Task Scheduler for Cloud and Fog Environments 2

OTEC: An Optimized Transcoding Task Scheduler for Cloud and ...

引用

2nd International Workshop on Design, Deployment, and Evaluation of Network-assisted Video Streaming (ViSNext) Part of 18th International Conference on Emerging Networking Experiments and Technologies (ACM CoNEXT)

作者： Afzal, Samira Tashtarian, Farzad Hadian, Hamid Erfanian, Alireza Timmerer, Christian Prodan, Radu Alpen Adria Univ Klagenfurt Inst Informat Technol ITEC Klagenfurt Austria Alpen Adria Univ Klagenfurt Christian Doppler Lab ATHENA Klagenfurt Austria

ISBN: (纸本)9781450399364

Encoding and transcoding videos into multiple codecs and representations is a significant challenge that requires seconds or even days on high-performance computers depending on many technical characteristics, such as video complexity or encoding parameters. Cloud computing offering on-demand computing resources optimized to meet the needs of customers and their budgets is a promising technology for accelerating dynamic transcoding workloads. In this work, we propose OTEC, a novel multi-objective optimization method based on the mixed-integer linear programming model to optimize the computing instance selection for transcoding processes. OTEC determines the type and number of cloud and fog resource instances for video encoding and transcoding tasks with optimized computation cost and time. We evaluated OTEC on AWS EC2 and Exoscale instances for various administrator priorities, the number of encoded video segments, and segment transcoding times. The results show that OTEC can achieve appropriate resource selections and satisfy the administrator's priorities in terms of time and cost minimization.

关键词： transcoding Scheduler Cloud Fog Optimization multi-objective MILP

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Throughput of Video transcoding in Cloud Services

Evaluating the Throughput of Video Transcoding in Cloud Serv...

引用

Data Compression Conference (DCC)

作者： Cai, Yangang Li, Xufeng Wang, Zhenyu Wang, Ronggang Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

In this paper, we propose a method to evaluate the throughput of video transcoding in cloud services. This method can quickly estimate the maximum number of transcoding video concurrence on the current transcoding unit.

ISBN: (纸本)9781665478939

关键词： Data compression transcoding Throughput

来源：评论

学校读者我要写书评

暂无评论

Framework for Experiential transcoding of Web Pages with Scanpath Trend Analysis 22

Framework for Experiential Transcoding of Web Pages with Sca...

引用

19th International Web for All Conference

作者： Trabzon, Idil Ece Yagiz, Furkan Karadavut, Elmas Eda Elhewahey, Mahmoud Eraslan, Sukru Yesilada, Yeliz Harper, Simon Middle East Tech Univ Northern Cyprus Campus Mersin 10 Guzelyurt Turkey Univ Manchester Dept Comp Sci Manchester Lancs England

ISBN: (纸本)9781450391702

Web pages consist of different visual elements, such as menus, sidebar and highlighted textual content. Although previous research shows that such elements help sighted users to complete their tasks, they can easily be distractive to users who access pages in audio. In particular, for visually-disabled users who use screen readers, such elements can mean that they need to listen to clutter to reach the desired content. In our previous work, we show that experiential transcoding can address this problem where web pages are transcoded by considering user experience. Trending elements and their order of visit are first identified by analyzing the eye movements of sighted people with Scanpath Trend Analysis (STA) and then the web pages can be transcoded by removing non-trending elements and re-ordering the trending ones based on the order they are visited. In this paper, we present our ongoing work on the development of a framework to automate and support experiential transcoding.

关键词： transcoding web-page elements STA VIPS browser extension

来源：评论

学校读者我要写书评

暂无评论

Soft Actor-Critic DRL for Live transcoding and Streaming in Vehicular Fog-Computing-Enabled IoV

引用

IEEE INTERNET OF THINGS JOURNAL 2021年第3期8卷 1308-1321页

作者： Fu, Fang Kang, Yunpeng Zhang, Zhicai Yu, F. Richard Wu, Tuan Shanxi Univ Sch Phys & Elect Engn Taiyuan 030006 Peoples R China Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China

With the rapid development of automotive industry and telecommunication technologies, live streaming services in the Internet of Vehicles (IoV) play an even more crucial role in vehicular infotainment systems. However, it is a big challenge to provide a high quality, low latency, and low bitrate variance live streaming service for vehicles due to the dynamic properties of wireless resources and channels of IoV. To solve this challenge, we propose a novel live video transcoding and streaming scheme that maximizes the video bitrate and decreases time-delays and bitrate variations in vehicular fog-computing (VFC)-enabled IoV, by jointly optimizing vehicle scheduling, bitrate selection, and computational/spectrum resource allocation. This joint optimization problem is modeled as a Markov decision process (MDP), considering time-varying characteristics of the available resources and wireless channels of IoV. A soft actor-critic deep reinforcement learning (DRL) algorithm that is based on the maximum entropy framework, is subsequently utilized to solve the above MDP. Extensive simulation results based on the data set of the real world show that compared to other baseline algorithms, the proposed scheme can effectively improve video quality while decreasing latency and bitrate variations, and access excellent performance in terms of learning speed and stability.

关键词： Streaming media Bit rate transcoding Edge computing Resource management Video recording Quality assessment Deep reinforcement learning (DRL) fog computing Internet of Vehicles (IoV) soft actor– critic video transcoding

来源：评论

学校读者我要写书评

暂无评论

Towards 5G: Joint Optimization of Video Segment Caching, transcoding and Resource Allocation for Adaptive Video Streaming in a Multi-Access Edge Computing Network

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第10期70卷 10909-10924页

作者： Huang, Xinyu He, Lijun Wang, Liejun Li, Fan Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China Xinjiang Univ Coll Informat Sci & Engn Urumqi 830046 Peoples R China

Caching and transcoding at multi-access edge computing (MEC) server and wireless resource allocation in eNodeB interact with each other and together determine the quality of experience (QoE) of dynamic adaptive streaming over HTTP (DASH) clients. However, the relationship among the three factors has not been explored, which has led to limited improvement in clients' QoE. Therefore, we propose a joint optimization framework of video segment caching and transcoding in MEC servers and resource allocation to improve the QoE of DASH clients. Based on the established framework, we develop an MEC caching management mechanism that consists of the MEC caching partition, video segment deletion, and MEC caching space transfer. Then, a joint optimization algorithm that combines the video segment caching and transcoding in the MEC server and resource allocation is proposed. In the algorithm, the clients' channel state and the playback status and cooperation among MEC servers are employed to estimate the client's priority, video segment representation switch and continuous playback time. Considering the above four factors, we develop a utility function model of clients' QoE. Then, we formulate a mixed-integer nonlinear programming mathematical model to maximize the total utility of DASH clients, where the video segment caching and transcoding strategy and resource allocation strategy are jointly optimized. To solve this problem, we propose a low-complexity heuristic algorithm that decomposes the original problem into multiple subproblems. The simulation results show that our proposed algorithms efficiently improve client's throughput, received video quality and hit ratio of video segments while decreasing the playback rebuffering time, video segment representation switch and system backhaul traffic.

关键词： Streaming media Servers Resource management transcoding Optimization Quality of experience Wireless communication MEC QoE video segment caching and transcoding resource allocation cooperation among MEC servers Branch and Bound

来源：评论

学校读者我要写书评

暂无评论

Augmented Queue-Based Transmission and transcoding Optimization for Livecast Services Based on Cloud-Edge-Crowd Integration

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第11期31卷 4470-4484页

作者： Chen, Xingyan Xu, Changqiao Wang, Mu Wu, Zhonghui Zhong, Lujie Grieco, Luigi Alfredo Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Capital Normal Univ Informat Engn Coll Beijing 100048 Peoples R China Politecn Bari Dept Elect & Informat Engn I-70126 Bari Italy Consorzio Nazl Interuniv Telecomunicaz CNIT I-00133 Rome Italy

Nowadays, amateur broadcasters can massively generate video contents and stream them across the Internet. For this reason, crowdsourced livecast services (CLS) are attracting millions of users around the world. To provide a smooth and high-quality playback experience to viewers with diversified device configurations in dynamic network conditions, CLS providers have to find a way to deploy cost-effective transcoding operations by distributing the computation-intensive workload among Cloud, Edge, and Crowd. In addition, it is necessary to control transcoded streams from million broadcasters to worldwide viewers. To address these challenges, we propose a novel stochastic approach that jointly optimizes the usage of transmission resources (e.g., bandwidth), and transcoding resources (e.g., CPU) in CLS systems that leverage the cooperation of Cloud, Edge, and Crowd technologies. In particular, we first design an augmented queue structure that can jointly capture the dynamic features of data transmission and online transcoding, based on the virtual queue technology. Then, we formulate a joint resource allocation problem, using stochastic optimization arguments, and devise an Accelerated Gradient Optimization (AGO) algorithm to solve the optimization problem in a scalable way. Moreover, we provide four main theoretical results that characterize the algorithm's steady-state queue-length, optimality, and fast-convergence. By conducting both numerical simulations and system-level evaluations based on our prototype, we demonstrate that our solution provides lower system costs and higher QoE performance against state-of-the-art solutions.

关键词： transcoding Cloud computing Optimization Streaming media Stochastic processes Resource management Delays Video transcoding transmission crowdsourced livecast services cloud computing edge computing crowd

来源：评论

学校读者我要写书评

暂无评论

LwTE: Light-Weight transcoding at the Edge

引用

IEEE ACCESS 2021年 9卷 112276-112289页

作者： Erfanian, Alireza Amirpour, Hadi Tashtarian, Farzad Timmerer, Christian Hellwagner, Hermann Alpen Adria Univ Klagenfurt Inst Informat Technol ITEC Christian Doppler Lab ATHENA A-9020 Klagenfurt Austria

Due to the growing demand for video streaming services, providers have to deal with increasing resource requirements for increasingly heterogeneous environments. To mitigate this problem, many works have been proposed which aim to (i) improve cloud/edge caching efficiency, (ii) use computation power available in the cloud/edge for on-the-fly transcoding, and (iii) optimize the trade-off among various cost parameters, e.g., storage, computation, and bandwidth. In this paper, we propose LwTE, a novel L ight- w eight T ranscoding approach at the E dge, in the context of HTTP Adaptive Streaming (HAS). During the encoding process of a video segment at the origin side, computationally intense search processes are going on. The main idea of LwTE is to store the optimal results of these search processes as metadata for each video bitrate and reuse them at the edge servers to reduce the required time and computational resources for on-the-fly transcoding. LwTE enables us to store only the highest bitrate plus corresponding metadata (of very small size) for unpopular video segments/bitrates. In this way, in addition to the significant reduction in bandwidth and storage consumption, the required time for on-the-fly transcoding of a requested segment is remarkably decreased by utilizing its corresponding metadata;unnecessary search processes are avoided. Popular video segments/bitrates are being stored. We investigate our approach for Video-on-Demand (VoD) streaming services by optimizing storage and computation (transcoding) costs at the edge servers and then compare it to conventional methods (store all bitrates, partial transcoding). The results indicate that our approach reduces the transcoding time by at least 80% and decreases the aforementioned costs by 12% to 70% compared to the state-of-the-art approaches.

关键词： transcoding Bit rate Streaming media Metadata Servers Bandwidth Edge computing Video streaming transcoding video on demand edge computing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：