The Experts below are selected from a list of 225612 Experts worldwide ranked by ideXlab platform

Raj Jain - One of the best experts on this subject based on the ideXlab platform.

  • High-Definition Video Streams Analysis, Modeling, and Prediction
    Advances in Multimedia, 2012
    Co-Authors: Abdel Karim Al Tamimi, Raj Jain
    Abstract:

    High-Definition Video streams' unique statistical characteristics and their high bandwidth requirements are considered to be a challenge in both network scheduling and resource allocation fields. In this paper, we introduce an innovative way to model and predict High-Definition (HD) Video traces encoded with H.264/AVC encoding standard. Our results are based on our compilation of over 50 HD Video traces. We show that our model, simplified seasonal ARIMA (SAM), provides an accurate representation for HD Videos, and it provides significant improvements in prediction accuracy. Such accuracy is vital to provide better dynamic resource allocation for Video traffic. In addition, we provide a statistical analysis of HD Videos, including both factor and cluster analysis to support a better understanding of Video stream workload characteristics and their impact on network traffic. We discuss our methodology to collect and encode our collection of HD Video traces. Our Video collection, results, and tools are available for the research community.

  • statistical analysis and modeling of high definition Video traces
    International Conference on Multimedia and Expo, 2010
    Co-Authors: Abdel Karim Al Tamimi, Raj Jain, Chakchai Soin
    Abstract:

    High definition Video streams are gaining larger shares of the Internet usage for typical users on daily basis. This is an expected result of the current boom in the online standard and high definition (HD) Video streaming services such as YouTube and Hulu. Because of these Video streams' unique statistical characteristics and their high bandwidth requirements, they are considered to be a continuous challenge in both network scheduling and resource allocation fields. In this paper we provide a statistical analysis of over 50 high definition Video traces that resembles wide varieties of high definition Video traffic workloads. We performed both factor and cluster analysis on our collection of Video traces to support a better understanding of Video stream workload characteristics and their impact on network traffic. Additionally, we compare and evaluate different modeling approaches for high definition Videos traces.

  • ICME - Statistical analysis and modeling of high definition Video traces
    2010 IEEE International Conference on Multimedia and Expo, 2010
    Co-Authors: Abdel Karim Al Tamimi, Raj Jain
    Abstract:

    High definition Video streams are gaining larger shares of the Internet usage for typical users on daily basis. This is an expected result of the current boom in the online standard and high definition (HD) Video streaming services such as YouTube and Hulu. Because of these Video streams' unique statistical characteristics and their high bandwidth requirements, they are considered to be a continuous challenge in both network scheduling and resource allocation fields. In this paper we provide a statistical analysis of over 50 high definition Video traces that resembles wide varieties of high definition Video traffic workloads. We performed both factor and cluster analysis on our collection of Video traces to support a better understanding of Video stream workload characteristics and their impact on network traffic. Additionally, we compare and evaluate different modeling approaches for high definition Videos traces.

Abdel Karim Al Tamimi - One of the best experts on this subject based on the ideXlab platform.

  • High-Definition Video Streams Analysis, Modeling, and Prediction
    Advances in Multimedia, 2012
    Co-Authors: Abdel Karim Al Tamimi, Raj Jain
    Abstract:

    High-Definition Video streams' unique statistical characteristics and their high bandwidth requirements are considered to be a challenge in both network scheduling and resource allocation fields. In this paper, we introduce an innovative way to model and predict High-Definition (HD) Video traces encoded with H.264/AVC encoding standard. Our results are based on our compilation of over 50 HD Video traces. We show that our model, simplified seasonal ARIMA (SAM), provides an accurate representation for HD Videos, and it provides significant improvements in prediction accuracy. Such accuracy is vital to provide better dynamic resource allocation for Video traffic. In addition, we provide a statistical analysis of HD Videos, including both factor and cluster analysis to support a better understanding of Video stream workload characteristics and their impact on network traffic. We discuss our methodology to collect and encode our collection of HD Video traces. Our Video collection, results, and tools are available for the research community.

  • statistical analysis and modeling of high definition Video traces
    International Conference on Multimedia and Expo, 2010
    Co-Authors: Abdel Karim Al Tamimi, Raj Jain, Chakchai Soin
    Abstract:

    High definition Video streams are gaining larger shares of the Internet usage for typical users on daily basis. This is an expected result of the current boom in the online standard and high definition (HD) Video streaming services such as YouTube and Hulu. Because of these Video streams' unique statistical characteristics and their high bandwidth requirements, they are considered to be a continuous challenge in both network scheduling and resource allocation fields. In this paper we provide a statistical analysis of over 50 high definition Video traces that resembles wide varieties of high definition Video traffic workloads. We performed both factor and cluster analysis on our collection of Video traces to support a better understanding of Video stream workload characteristics and their impact on network traffic. Additionally, we compare and evaluate different modeling approaches for high definition Videos traces.

  • ICME - Statistical analysis and modeling of high definition Video traces
    2010 IEEE International Conference on Multimedia and Expo, 2010
    Co-Authors: Abdel Karim Al Tamimi, Raj Jain
    Abstract:

    High definition Video streams are gaining larger shares of the Internet usage for typical users on daily basis. This is an expected result of the current boom in the online standard and high definition (HD) Video streaming services such as YouTube and Hulu. Because of these Video streams' unique statistical characteristics and their high bandwidth requirements, they are considered to be a continuous challenge in both network scheduling and resource allocation fields. In this paper we provide a statistical analysis of over 50 high definition Video traces that resembles wide varieties of high definition Video traffic workloads. We performed both factor and cluster analysis on our collection of Video traces to support a better understanding of Video stream workload characteristics and their impact on network traffic. Additionally, we compare and evaluate different modeling approaches for high definition Videos traces.

Chongmin Kyung - One of the best experts on this subject based on the ideXlab platform.

  • a lossless embedded compression algorithm for high definition Video coding
    International Conference on Multimedia and Expo, 2009
    Co-Authors: Chongmin Kyung
    Abstract:

    With a rapid increase of image resolution in recent mobile Video systems, the memory bandwidth and power consumption have surfaced as critical problems in Video coding. Despite many embedded compression algorithms proposed to overcome these problems, no proper algorithms have yet been reported with high enough throughput and no quality loss. This paper first proposes a lossless embedded compression algorithm with high compression ratio along with the hardware architecture for high throughput. Experimental results show that the proposed algorithm achieves at least 60% data compression without quality loss and bitrate increment. Moreover, the latency of the proposed architecture is sufficient to meet the real-time requirement of High-Definition Video coding, i.e., 30 cycles/macroblock. The proposed algorithm can be implemented with about 28K gates.

  • ICME - A lossless embedded compression algorithm for high definition Video coding
    2009 IEEE International Conference on Multimedia and Expo, 2009
    Co-Authors: Chongmin Kyung
    Abstract:

    With a rapid increase of image resolution in recent mobile Video systems, the memory bandwidth and power consumption have surfaced as critical problems in Video coding. Despite many embedded compression algorithms proposed to overcome these problems, no proper algorithms have yet been reported with high enough throughput and no quality loss. This paper first proposes a lossless embedded compression algorithm with high compression ratio along with the hardware architecture for high throughput. Experimental results show that the proposed algorithm achieves at least 60% data compression without quality loss and bitrate increment. Moreover, the latency of the proposed architecture is sufficient to meet the real-time requirement of High-Definition Video coding, i.e., 30 cycles/macroblock. The proposed algorithm can be implemented with about 28K gates.

  • ICME - A lossless embedded compression algorithm for high definition Video coding
    2009 IEEE International Conference on Multimedia and Expo, 2009
    Co-Authors: Chongmin Kyung
    Abstract:

    With a rapid increase of image resolution in recent mobile Video systems, the memory bandwidth and power consumption have surfaced as critical problems in Video coding. Despite many embedded compression algorithms proposed to overcome these problems, no proper algorithms have yet been reported with high enough throughput and no quality loss. This paper first proposes a lossless embedded compression algorithm with high compression ratio along with the hardware architecture for high throughput. Experimental results show that the proposed algorithm achieves at least 60% data compression without quality loss and bitrate increment. Moreover, the latency of the proposed architecture is sufficient to meet the real-time requirement of High-Definition Video coding, i.e., 30 cycles/macroblock. The proposed algorithm can be implemented with about 28K gates.

Mojtaba Hosseini - One of the best experts on this subject based on the ideXlab platform.

  • ICME - A rate control algorithm for ×264 high definition Video conferencing
    2011 IEEE International Conference on Multimedia and Expo, 2011
    Co-Authors: Abbas Javdtalab, Mona Omidyeganeh, Shervin Shirmohammandi, Mojtaba Hosseini
    Abstract:

    Current rate control algorithms developed in the H.264 encoder are unable to address High Definition Video streaming requirements over best effort networks such as the Internet. We propose a rate controller (RC) for H.264 high definition Video conferencing (HDVC), specifically developed in the x264 codec: an open source and high performance H.264/AVC encoder. Called the Dynamic Constant Rate Factor (DCRF), our RC tries to provide the best possible quality in terms of the current bandwidth available at a given instance of Video streaming. The proposed method is analyzed from different aspects including network bandwidth, frame size, PSNR and SSIM measurements. In addition to the objective tests, DCRF is evaluated by human observers for subjective performance evaluation. The results prove that DCRF provides better results than current RC methods for HDVC.

  • On the suitability of current x264 rate controller algorithms for high definition Video conferencing
    2011 International Symposium on Artificial Intelligence and Signal Processing (AISP), 2011
    Co-Authors: Abbas Javadtalab, Mona Omidyeganeh, Shervin Shirmohammandi, Mojtaba Hosseini
    Abstract:

    In this paper we analyze current H.264 rate distortion algorithms (RD), specifically those developed in the x264 codec which is an open source and high performance H.264/AVC encoder, and evaluate their suitability for high definition Video conferencing (HDVC). Moreover, we report the current shortcomings of these methods and highlight issues to be addressed for future research, as well as the requirements of an RD for HDVC. Each method is analyzed from aspects such as network bandwidth, frame size, PSNR and SSIM measurements. In addition to the objective tests, the results are evaluated by human observers in order to get the correct perceptive of the performance of each method.

  • A RATE CONTROL ALGORITHM FOR x264 HIGH DEFINITION Video CONFERENCING
    2011
    Co-Authors: Abbas Javdtalab, Mona Omidyeganeh, Shervin Shirmohammandi, Mojtaba Hosseini
    Abstract:

    Current rate control algorithms developed in the H.264 encoder are unable to address High Definition Video streaming requirements over best effort networks such as the Internet. We propose a rate controller (RC) for H.264 high definition Video conferencing (HDVC), specifically developed in the x264 codec: an open source and high performance H.264/AVC encoder. Called the Dynamic Constant Rate Factor (DCRF), our RC tries to provide the best possible quality in terms of the current bandwidth available at a given instance of Video streaming. The proposed method is analyzed from different aspects including network bandwidth, frame size, PSNR and SSIM measurements. In addition to the objective tests, DCRF is evaluated by human observers for subjective performance evaluation. The results prove that DCRF provides better results than current RC methods for HDVC. Index Terms— Rate distortion, Average Bitrate, Video Conferencing, High Definition Video.

Søren Forchhammer - One of the best experts on this subject based on the ideXlab platform.