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检索条件"主题词=video transcoding"

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A Dynamic-Mode Redundant Coding for Error-resilient video transcoding

A Dynamic-Mode Redundant Coding for Error-resilient Video Tr...

引用

International Conference on Information Technology and Applications (ITA)

作者： Zhou, Yiran Zhang, Qi Ning, Shushi Dalian Maritime Univ Informat Sci & Technol Coll Dalian Peoples R China

ISBN: (纸本)9781479928767

An error-resilient video transcoding scheme is proposed in this paper, which dynamically chooses the proper redundant coding mode for the protection. To improve the efficiency of the bitrate increase, the redundant coding mode is determined dynamically by the rate-distortion performance calculation and the region-of-interest location. According to different scenarios, different numbers and locations of redundant slices are utilized. If needed, the redundant slice is generated by the entropy coding and embedded into the bitstream. Experiments demonstrate that new bitstream's quality can be improved by the proposal when transmitted in the unreliable environment.

关键词： Error resilience Redundant coding rate-distortiont video transcoding

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Distributed video transcoding and its application to grid delivery 9

Distributed video transcoding and its application to grid de...

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9th Asia-Pacific Conference on Communications held in conjunction with the 6th Malaysia International Conference on Communications (MICC 2003)

作者： Sambe, Y Watanabe, S Yu, D Nakamura, T Wakamiya, N NTTDATA Corp R&D Headquarters Tokyo Japan

ISBN: (纸本)0780381149

This paper describes a distributed video transcoding system that can transcode an MPEG-2 video file into various video coding formats with different rates, and its application to grid delivery of video. The transcoder divides the MPEG-2 file into small segments along the time axis and transcodes them in parallel at transcoding nodes. To cope with heterogeneity and time-varying performance of transcoding processors in grid computing environment, dynamic scheduling algorithm are proposed. The simulation results demonstrate that the proposed dynamic scheduling algorithm can decrease total transcoding time by 30% in uniform performance fluctuation environment, compared with static scheduling.

关键词： video transcoding MPEG grid computing P2P

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MULTIPASS ENCODING FOR REDUCING PULSING ARTIFACTS IN CLOUD BASED video transcoding

MULTIPASS ENCODING FOR REDUCING PULSING ARTIFACTS IN CLOUD B...

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IEEE International Conference on Image Processing (ICIP)

作者： Lin, Yao-Chung Denman, Hugh Kokaram, Anil Google Inc 1600 Amphitheatre Pkwy Mountain View CA 94043 USA

ISBN: (纸本)9781479983391

Low latency video transcoding is an important feature in video sharing platforms. Typically this is achieved by splitting a clip into short segments, followed by parallel encoding of the segments. However, this introduces quality artifacts due to transients in encoder rate control. We present a model for more effectively predicting the behavior of the encoder in each encoding pass, which minimizes this problem. We learn the model using measurements from more than 500 video clips, hence ensuring reliable estimation. Finally, we show that the proposed multipass strategy delivers more stable reconstructed quality for parallel video transcoding frameworks.

关键词： video transcoding multipass rate control

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Dynamic Resource Allocation for video transcoding with QoS Guaranteeing in Cloud-based DASH System

Dynamic Resource Allocation for Video Transcoding with QoS G...

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IEEE Global Communications Conference (GLOBECOM)

作者： Ran, Yongyi Shi, Youkang Yang, Enzhong Chen, Shuangwu Yang, Jian Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China

ISBN: (纸本)9781479974702

Due to diverse network conditions and heterogeneous devices, there may be various video demands with different video qualities and formats from the client side. Compared to keeping all necessary copies for the same video, video transcoding in real-time should be an essential solution. The complex nature of video transcoding enables cloud computing to be uniquely suitable for dynamically providing transcoding resource. However, due to the fluctuation and uncertainty of the future transcoding demand, it is still a challenge to dynamically determine the optimal resource allocation to save cost while guaranteeing the Quality of Service (QoS). Overload may result in the transcoding jitter and increase the lateness which directly affects video freezes while over-provisioning naturally increases the cost. To address this problem, in this paper, by defining the transcoding jitter probability as a metric of QoS, we proposed a dynamic resource allocation algorithm based on the large deviation principle, which is capable of proactive calculating the optimal number of transcoding nodes for the upcoming transcoding demand subject to the transcoding jitter probability below a desired threshold. Finally, the experiments are performed on a cloud-based prototype system to show the attainable performance of the proposed resource allocation algorithm and verify that the proposed algorithm can make a good tradeoff between cost saving and QoS guaranteeing.

关键词： Dynamic resource allocation video transcoding cloud computing quality of service cost saving

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Machine Learning Based VP9-to-HEVC video transcoding 12

Machine Learning Based VP9-to-HEVC Video Transcoding

引用

12th IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Li, Xiangyu Xie, Rong Song, Li Zhang, Liang Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Cooperat Medianet Innovat Ctr Shanghai Peoples R China

ISBN: (纸本)9781509049370

As more and more coding standards are developed for videos with higher resolution and higher frame rates, the conversion between them is in demand. This paper focuses on the development of fast VP9-to-HEVC transcoding algorithm. Since the decoding-and-full-re-encoding process is time consuming, we explore Naive-Bayes based machine learning algorithm to accelerate VP9-to-HEVC transcoding. The machine learning process can bridge VP9 decoding information and HEVC splitting decision throughout training results, so the HEVC CU searching process can be simplified for faster transcoding. Two new features closely related to VP9, sub-block counting and depth map, are added for VP9-to-HEVC video transcoder. Using coding features and Naive-Bayes algorithm, several models are built for different QPs and block sizes to reduce transcoding time. Experiments show that a maximum time reduction of 53% and an average reduction of 44% can be achieved and the loss of decoded image quality in BD-rate is almost ignorable.

关键词： VP9 HEVC video transcoding machine learning algorithm

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Stream-Based Admission Control and Scheduling for video transcoding in Cloud Computing 13

Stream-Based Admission Control and Scheduling for Video Tran...

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13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (CCGrid)

作者： Ashraf, Adnan Jokhio, Fareed Deneke, Tewodros Lafond, Sebastien Porres, Ivan Lilius, Johan Abo Akad Univ Dept Informat Technol Turku Finland

ISBN: (纸本)9780769549965;9781467364652

This paper presents a novel approach for stream-based admission control and job scheduling for video transcoding called SBACS (Stream-Based Admission Control and Scheduling). SBACS uses queue waiting time of transcoding servers to make admission control decisions for incoming video streams. It implements stream-based admission control with per stream admission. To ensure efficient utilization of the transcoding servers, video streams are segmented at the Group of Pictures level. In addition to the traditional rejection policy, SBACS also provides a stream deferment policy, which exploits cloud elasticity to allow temporary deferment of the incoming video streams. In other words, the admission controller can decide to admit, defer, or reject an incoming stream and hence reduce rejection rate. In order to prevent transcoding jitters in the admitted streams, we introduce a job scheduling mechanism, which drops a small proportion of video frames from a video segment to ensure continued delivery of video contents to the user. The approach is demonstrated in a discrete-event simulation with a series of experiments involving different load patterns and stream arrival rates.

关键词： video transcoding cloud computing admission control scheduling segmentation resource allocation

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Adaptive Rate Control for Dynamic Bandwidth in video transcoding

Adaptive Rate Control for Dynamic Bandwidth in Video Transco...

引用

11th IEEE Singapore International Conference on Communication Systems

作者： Wang, Xiaoyang Zhang, Yi Li, Hongliang Zhu, Weile Univ Elect Sci & Technol China Chengdu 610054 Peoples R China

ISBN: (纸本)9781424424238

To overcome the overflowing and information loss caused by huge volume compressed video transmitting over dynamic changeable bandwidth, in this work we propose a frame layer adaptive rate control scheme suitable for the video transcoding from mpeg2 to H.264 which is able to convert the bit rate of the transcoded bitstream according to the variable bandwidth. The original compressed information such as quantization parameter, input frame bit rate and the input and output bandwidth can be utilized to derive the new frame target bits. The proposed rate control strategy estimates the current transcoding frame targets bits employing rate complexity model which is capable of tracking input frame characteristic. Also the accumulated output frame complexity computed from the transcoded output video stream is considered to keep the constant quality without large motion jerkiness and quality flicker. In addition, a low complexity quantization parameter estimation model is proposed. Experimental results show the new rate control for video transcoding can achieve better video quality and more accurate video rates compared with the h.264 rate control scheme adopted by the cascaded transcoder. Also, the new adaptive rate control transcoder nearly improve speed 70% than the cascaded transcoder.

关键词： adapt bandwidth rate control video transcoding

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Architecture for Predicting Live video transcoding Performance on Docker Containers

Architecture for Predicting Live Video Transcoding Performan...

引用

IEEE International Conference on Services Computing (IEEE SCC) Part of the IEEE World Congress on Services

作者： Paakkonen, Pekka Heikkinen, Antti Aihkisalo, Tommi VTT Tech Res Ctr Finland Kaitovayla 1 Oulu 90571 Finland

ISBN: (纸本)9781538672501

video can be streamed live with different applications (e.g. YouTube Live, Periscope). Typically, the video content is adapted for end users based on receiving client's capabilities, and network bandwidth. The adaptation is realized with different video representations, which are created by transcoding the original video content. When video is streamed live, transcoding has to be completed within real time constraints, which is a computationally demanding process. Particularly, live transcoding should be enabled efficiently by a content distributor to minimize resource provisioning costs. The contribution of this paper is an architecture for predicting live video transcoding performance on a Docker-based platform. Particularly, cloud resource management for live video transcoding has been focused on. A model was trained based on measurements in different transcoding configurations. Offline evaluation results indicate that live transcoding speed or CPU usage can be predicted with 3-8 % accuracy. When video is transcoded on virtual machines based on predictions in a prototype system (live), live transcoding speed prediction accuracy is within a similar range as the offline performance, but worse for CPU usage prediction (5-15 %). In most cases the specified range for transcoding speed and CPU usage can be achieved at least with a precision of 76 %.

关键词： video transcoding FFmpeg Rancher Cassandra Docker Random forest Prometheus

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vbench: Benchmarking video transcoding in the Cloud 18

vbench: Benchmarking Video Transcoding in the Cloud

引用

23rd International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Lottarini, Andrea Ramirez, Alex Coburn, Joel Kim, Martha A. Ranganathan, Parthasarathy Stodolsky, Daniel Wachsler, Mark Columbia Univ New York NY 10027 USA Google Mountain View CA USA

ISBN: (纸本)9781450349116

This paper presents vbench, a publicly available benchmark for cloud video services. We are the first study, to the best of our knowledge, to characterize the emerging video-as-aservice workload. Unlike prior video processing benchmarks, vbench's videos are algorithmically selected to represent a large commercial corpus of millions of videos. Reflecting the complex infrastructure that processes and hosts these videos, vbench includes carefully constructed metrics and baselines. The combination of validated corpus, baselines, and metrics reveal nuanced tradeoffs between speed, quality, and compression. We demonstrate the importance of video selection with a microarchitectural study of cache, branch, and SIMD behavior. vbench reveals trends from the commercial corpus that are not visible in other video corpuses. Our experiments with GPUs under vbench's scoring scenarios reveal that context is critical: GPUs are well suited for live-streaming, while for video-on-demand shift costs from compute to storage and network. Counterintuitively, they are not viable for popular videos, for which highly compressed, high quality copies are required. We instead find that popular videos are currently well-served by the current trajectory of software encoders.

关键词： benchmark accelerator video transcoding

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Learning Based Fast H.264/AVC to HEVC INTRA video transcoding for Cloud Media Computing 3rd

Learning Based Fast H.264/AVC to HEVC INTRA Video Transcodin...

引用

3rd International Conference on Cloud Computing and Security (ICCCS)

作者： Zhang, Yun Li, Na Peng, Zongju Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Ningbo Univ Fac Informat Sci & Engn Ningbo 315211 Zhejiang Peoples R China

ISBN: (纸本)9783319685427;9783319685410

Cloud video transcoding enable to convert the video standards and properties from one to another so as to adapt to different user end devices and network capacity, especially in sharing massive video contents in cloud environment. High Efficiency video Coding (HEVC) and H.264/Advanced video Coding are two recent high performance video coding standards that are widely used and co-existing in video industry. video transcoding is desirable to bridge the standard gap. To effectively transcode video stream from H.264/AVC to HEVC for higher compression efficiency and meanwhile maintaining low computational complexity, a learning based fast H.264/AVC to HEVC transcoder is proposed for cloud media computing. We firstly analyze the correlation of block partition sizes between these two standards and then present a fast Coding Unit (CU) decision algorithm, in which three levels of binary classifiers are used to predict different CU sizes in HEVC intra coding and the optimal parameters are determined by statistical experiments. The experimental results show that the proposed transcoder achieves 44.3% time saving on average with only negligible quality degradation when compared with the original cascaded transcoder and is also superior than the state-of-the-art benchmarks in terms of complexity reduction and rate-distortion performance.

关键词： HEVC video transcoding Machine learning CU decision Cloud computing

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