A comparison of two parallel algorithms using predictive load balancing for video compression

Size: px
Start display at page:

Download "A comparison of two parallel algorithms using predictive load balancing for video compression"

Transcription

1 A comparison of two parallel algorithms using predictive load balancing for video compression CARLOS-JULIAN GENIS-TRIANA 1, ABELARDO RODRIGUEZ-LEON 2 and RAFAEL RIVERA- LOPEZ 2 Departamento de Sistemas y Computación Instituto Tecnológico de Veracruz Calzada M. A. de Quevedro 2779, Col. Formando Hogar, Veracruz, Ver. MÉXICO 1 2 Abstract:- This paper shows a comparison of two parallel algorithms for video compression that use predictive load balancing. One algorithm uses an exhaustive estimation of movements and the other one has an adaptive scheme. These algorithms are based on the H.264 standard of video compression and they are constructed using a Group of Pictures (GOP) level of parallelization. These algorithms are evaluated to determine their speedup when they are executed with a different number of nodes in a cluster processing videos of different types and resolutions. This evaluation is used for determining the equation that is used for calculating the number of nodes required to obtain a video compression in real time. Key-Words: - parallel programming, load balancing, video compression. 1 Introduction In recent years, several standards for video compression have been developed (H.264 [1], MPEG4 [2], JPEG2000 [3]), each one with advantages and drawbacks. The main challenge for video compression is to reduce the size of the transmitted video without to loss its quality. Video compression requires high performance systems that permits to reduce the compression-time, and it is in this moment that the multiprocessor systems (tightly-coupled or looselycoupled) are required. The use of these systems (clusters by example) for video compression requires the implementation of applications using parallel programming [4]. There are several approaches for parallel programing, but Message Passing Interface (MPI) [5] is a de facto standard for communication among processes that model a parallel program running on a distributed memory system. Digital video consists of a sequence of images (frames) and the effect of motion (small and continuous changes) between frames. A sequence of frames is called a "group of pictures" (GOP). In video compression, GOPs are processed for removing the similarities between consecutive video frames or for reducing irrelevant and redundant information in frames. This redundant information is deleted for the application of predictive models, sensory redundancy or statistic redundancy [6]. Predictive model uses two methods for frame codification: intra-frame, when pixels in a frame are encoded using only the information of this frame, and inter-frame, when a frame is encoded using the information of contiguous frames. Frames encoded using infra-frame codification are called I-frames, frames that are encoded using only a previously displayed reference frame are called P-frames and frames that are encoded using both future and previously displayed reference frames are called Bframes. This paper focuses on developing a comparison between two parallel algorithms for the video compression that use predictive load balancing. These algorithms are based on the H.264 standard of video compression and they are constructed using a parallelization of GOPs. Load distribution used in these algorithms uses two schemes: First, a preassignment scheme is applied for the initial allocation of GOPs in nodes of a cluster, then, an on-demand distribution scheme is applied, constructing a schedule based on the complexity analysis of encoding the next GOPs. This schedule sends a heavy GOP to the node that previously codified a lighter GOP. These algorithms are predictive algorithms with motion estimation. One algorithm uses an exhaustive estimation of movements and the other one has an adaptive scheme. These algorithms are evaluated to determine their speedup when they are executed with a different number of nodes of a cluster processing videos of different types and resolutions. This evaluation is used for determining the equation that is used for calculating the number of nodes required to obtain a video compression in real time. ISBN:

2 The rest of this paper is organized as follows. In the next section, we describe the predictive load balancing schemes used for these algorithms. Section 3 includes a description of the predictive parallel algorithm with exhaustive motion estimation. Section 4 describes the predictive parallel algorithm with adaptive motion estimation. Finally, we summarize the results of this work in Section 5. 2 Predictive load balancing schemes The parallelization approach defined in this work uses a video stream that is divided in GOPs of 15 frames. In this approach, a GOP has the pattern IBBPBBPBBPBBP [7]. In this pattern, a I-frame indicates the start of a new GOP in the video sequence. The use of this pattern avoids the loss of quality produced in an inter-frame codification and it permits that each GOP can be encoded in different nodes of a cluster. 2.1 Pre-assigment scheme This scheme is used to assign the first group of GOPs to the nodes in a cluster. The computational cost for encoding frames is variable and unpredictable. For a cluster with n_proc processors, each processor is identified by a number between 0 and n_proc-1. The number of GOPs (n_gops) is divided by n_proc, and this value is identify as n_gop_v. Each processor receives n_gop_v GOPs for encoding. If n_gops is not multiple of n_proc, the remaining GOPs (n) are assigned to first n processors. Figure 1 shows a state graph for pre-assigment scheme. Table 1 shows a description of transitions in figure 1. Fig. 1.- Pre-assigment scheme for GOPs. 2.2 On-demand distribution scheme In an on-demand distribution scheme, the nodes of the cluster receive the next GOP when it has finished its compression. In this scheme, a dealer-node is selected for coordinating the GOP assignment to the free nodes (encoder-nodes). The objective of this scheme is to reduce the idle time in cluster processors. Table 1.- Transitions in pre-assigment scheme. Transition Description T.1.1,, T.N.1 Processor 1,, N calculates the GOP to encode. T.1.2,, T.N.2 Processor 1,, N reads the GOP of disk. T.1.3,, T.N.3 Processor 1,, N encodes the GOP. T.1.4,, T.N.4 Processor 1,, N writes the encoded GOP. In figure 2 is showed the algorithm used for this distribution scheme. Figure 3 shows a graph for the on-demand distribution scheme and table 3 shows a description of the transitions in figure 3. procedure on_demand_distribution{ for(n_gop = 0 to n_gops){ if(n_gop < n_proc) proc = n_gop; else proc = wait_for_processor(); Gop_codification(proc,n_Gop); } halt_all_processors(); write_codified_gops(); } Fig. 2.- GOPs on-demand distribution algorithm. Fig. 3.- GOPs on-demand distribution scheme. 3 Predictive parallel algorithm with exhaustive motion estimation This algorithm distributes the GOPs using a prediction process applied in each encoder-node. This algorithm uses an exhaustive motion estimation because it searches for blocks with all possible sizes used for the compression process. Table 2.- Transitions in on-demand distribution scheme. Transition Description T.1.0,, T.N.0 Processor 1,, N notifies that is available. T.1.1,, T.N.1 Processor 1,, N determines the GOP to encode. T.1.2,, T.N.2 Processor 0 notifies to processor 1,, N the number GOP to be encoded. T.1.3,, T.N.3 Processor 1,, N reads the GOP of disk. T.1.4,, T.N.4 Processor 1,, N encodes the GOP. T.1.5,, T.N.5 Processor 1,, N writes the encoded GOP. ISBN:

3 In this algorithm, the dealer-node (processor 0) assigns the GOPs to the encoder-nodes (processors 1,, N). Encoder-nodes read of disk the GOP assigned and starts the compression process. Before of to finish this compression process, each encoder-node sends to the dealer-node one estimated-time for to finish its compression process. Just before to finish the compression process in encoder-nodes, dealer-node sends the next GOP number to be codified at each encoder-node, with the aim of to reduce the idle time. This algorithm defines two elements for this prediction process: the GOPwindow and the prediction scheme for the compression-time. 3.1 GOP-window A GOP-window is the size of a group of GOPs in relation with the number of encoder-nodes. In (1) is defined the value of GOP-window (n_gop_v) when n_gops is the number of GOPs in the video stream and n_proc is the number of encoder-nodes in a cluster. (1) Each encoder-node is assigned to one GOPwindow. In each step of codification process, the encoder-nodes are interchanged into the GOPwindows. The load balancing is obtained due that the allocation of encoder-node to the GOP-window is based on the computational cost of the GOP compression-time. In figure 4 is showed an example with three iterations of the assignment process, using nine GOPs grouped in three GOP-windows and using three encoder-nodes. Fig. 4.- A example with three rounds for GOP-window assignment. In this example, for the first round of assignment, the heavier GOP-window is the number 2, and the lightest GOP-window is the number 1. When the first round is finished, the encoder-nodes 1 and 2 are swapped in their GOP-windows. In the second round of assignment, the heavier GOP-window is the number 3, and the lightest GOP-window is the number 2. When this round is finished, the encoder-nodes 1 and 3 are swapped in their GOP-windows. 3.2 Prediction scheme of the compression-time For predicting the compression-time of one GOP it is necessary to use 16 frames of video stream, but only 15 frames will be stored. Frame 16 is necessary because the frame 15 is a B-frame and its uses both future and previously frames into video stream in its compression process. I-frame only uses one frame in its compression, and P-frame uses two frames in its compression. Figure 5 shows the compression sequence for the IBBPBBPBBPBBPI pattern. Sequence Frame Type I P B B P B B P B B P B B I B B Frame Number in GOP Fig. 5.- Compression sequence of 16 frames. The prediction scheme is based on sending two messages to the dealer-node: Estimated-time message: The estimated-time is calculate using the average compression time of each type of frame encoded in a GOP. In the compression process, when the frame 12 is encoded, the estimated-time is calculated. The estimated-time is defined in (2). t est = t I avgi r + t P avgp r + t B avgb r (2) In (2), t est is the estimated time, t I avg is the average compression time for I-frames, I r is the number of I-frames uncoded, t P avg is the average compression time for P-frames, P r is the number of P-frames uncoded, t B avg is the average compression time for B-frames, and B r is the number of B-frames uncoded. Finish-compression message: When the encodernode has processed the frame 15, the dealer-node is notifed that is finished. Then, the dealer-node identifies the more complex GOP-window and a encoder-node is assigned. Dealer-node receives the estimated-time of each ISBN:

4 encoder-node, the shortest estimated-time received is used by the dealer-node as a timeout for starting the scheduling process. This scheduling process assigns the encoder-nodes to the GOP-windows using the estimated-times received. Then the encoder-node that finished first is assigned to the heavier GOP-window. Figure 6 shows a graph for predictive parallel algorithm with exhaustive motion estimation and table 4 shows a description of transitions in figure 6. - E2: Search using only 16x16 blocks. In a low-motion video, a 16x16 block is sufficient for encoding the video without quality loss. In other hand, in an high-motion video, it is necessary to explore with several blocks sizes. When a video stream is compressed using few blocks, the compression process is faster but the main problem is to define, a priori, the motion conditions in a video stream. In [8] is defined a adaptive level, identified as E-A, that uses a manual assignment of precision levels. This algorithm was tested using two video streams (Foreman CIF and Stockholm HDTV). In figures 7, 8 and 9 are showed the comparison of compressiontime, quality-loss and bit-rate increment for these adaptive levels. Fig. 6.- Predictive parallel scheme with exhaustive motion estimation. Table 4.- Transitions in predictive parallel scheme with exhaustive motion estimation. Transition Description T.0 Processor 0 defines the GOP assignment of first round. T.1.0,..., T.N.0 Processor 0 notifies to processor N the number of GOP to encode. T.1.1,, T.N.1 Processor 1,, N reads the GOP of disk. T.1.2,, T.N.2 Processor 1,, N encodes the GOP. T.1.3,, T.N.3 Processor 1,..., N sends its prediction-time. T.1.4,, T.N.4 Processor 0 stores the prediction-time. T.1.5 Processor 0 sets the Timer T.M Timer starts the scheduling. T.0 Processor 0 defines the GOP assignment of second round. T.1.6,, T.N.6 Processor 1,, N writes the encoded GOP. 4 Predictive parallel algorithm with adaptive motion estimation This algorithm is designed for reducing the computational time of the motion estimation [x8]. H.264 standard permits to define the characteristics of motion estimation using several parameters. Using these parameters it is possible to define several precision levels. In this case, three levels are used: - E0: Exhaustive search. - E1:Search using 16x16, 16x8, 8x16 and 8x8 blocks. Fig. 7.- Comparison of compression time. Fig. 8.- Comparison of quality loss. Fig. 9.- Comparison of bit rate increment. ISBN:

5 These results suggest that the use of an adaptive motion estimation is one interesting approach because it reduces the compression time (15%), and the quality-loss and the bit-rate increment are comparable with the other approaches. 5 Performance analysis Tests of these predictive parallel algorithms were realized in the Mozart cluster. Mozart has 4 biprocessor nodes with AMD Opteron 246 at 2 GHz interconnected by a switched Gigabit Ethernet. The video sequence used is the Riverbed HDTV. The test sequences of Riverbed have three image resolutions: 720x480, 1280x720 and 1920x1088. Each algorithm is evaluated using 2, 4 and 8 processors and their efficiency is determined (table 5). In this table, it is observed that the average efficiency is similar for all video resolutions and all number of processors. To estimate the number of processors required for realtime encoding, the average efficiency (0.9524) obtained in this analysis is used. Table 5.- Efficiency of predictive algorithm. Resolution 2P 4P 8P Average 720x x x Average To achieve real-time encoding in a cluster is necessary that the encoded GOPs per second is comparable with the GOPs per second generated by the video source. The estimation of number of processor is based on the aplication of Little's law [9], showed in equation (3). In this formulation, a job is the process of to encode one GOP in one processor. The equation terms are: N = X*R (3) N: Number of GOPs processed in a cluster, number of processors. R: Compression-time of a single GOP in a cluster. X: Number of GOPs encoded per second (compression productivity). In this analysis, is the parallel compression time, R SEC is the sequential compression time, L is the number of GOPs to be encoded, Sp is the SpeedUp, E G is the efficiency. Then, the parallel compression productivity is defined in (4), the sequential productivity is showed in (5), the speedup is defined in (6) and the efficiency in (7). Sp= L X SEC = L E G = Sp N = = N (4) X SEC = 1 R SEC (5) X SEQ = N R SEQ N N 1 R SEQ = N R SEQ (6) = R SEQ (7) Using equation (7), the parallel compression time is defined in (8) and, using the Little's law, the number of processors is defined in (9). N = = R SEQ E G (8) = R SEQ E G (9) For to achieve real-time compression are needed to encode 30 frames per second (2 GOPs per second). If, by example, the sequential compression time (R SEC ) for encoding 15 frames with resolution of 720x480 is seconds, and it is used the average efficiency for this resolution in table 5 (E G = 0.95), the number of processors required is : N = = 531 processors Conclusions In this paper is presented a comparison between two parallel algorithms using predictive load balancing for video compression, the adaptive approach reduces 15% the compression-time in relation with the algorithm with exhaustive estimation of movements. The principal drawback of adaptive motion estimation is the complexity of determining the block size to ensure a little visible quality loss based on the conditions of movement of the video stream. Using the Little's law, it is possible to determine the number of processor required for to produce real time compression. As future work, it is necessary to realize more exhaustive tests for to determine the scalability of the algorithm. ISBN:

6 References: [1] T. Wiegand, G.J. Sullivan, G. Bjontegaard and A. Luthra: Overview of the H.264 / AVC Video Coding Standard, in IEEE Trans. on Circuits and Systems for Video Technology, vol. 13, no. 7, 2003, pp [2] T. Ebrahimi, C. Horne: MPEG-4 natural video coding - An overview, in Signal Processing: Image Communication, 15, 2000, pp [3] M. J. Gormish, D. Lee, and M.W. Marcellin: JPEG 2000: Overview, architecture and applications, in Proc. IEEE Int. Conf. Image Processing, Vancouver, Canada, 2000, vol. II, pp [4] J.C. Fernandez, M.P. Malumbres: A parallel implementation of H.26L video encoder, in Lectures Notes on Computer Sciencies, vol. 2400, pp , [5] D. W. Walker: The Design of a Standard Message Passing Interface for Distributed Memory Concurrent Computers, in Parallel Computing, vol. 20, no. 4, pp , [6] I. E. G. Richardson: Video Coding Design, Wiley, [7] A. Rodriguez, A. González, M.P. Malumbres.: Diseño de un Algoritmo Paralelo para codificación de Vídeo MPEG-4 sobre un cluster de Computadoras Personales, in Memorias del 9o. Congreso Internacional de Investigación en Ciencias Computacionales, Puebla, Mexico, pp , [8] A. Rodriguez-Leon, M. Perez, A. Gonzalez, J. Peinado, J.C. Fernandez: Paralelización del codificador H.264 con estimación de movimiento adaptiva en clusters de PCs, in Actas de las XVI Jornadas de Paralelismo, JP2005, pp , [9] J. D. C. Little: A Proof of the Queuing Formula: L =λw, in Operations Research, Vol. 9, No. 3, pp , ISBN:

Study and Implementation of Video Compression standards (H.264/AVC, Dirac)

Study and Implementation of Video Compression standards (H.264/AVC, Dirac) Study and Implementation of Video Compression standards (H.264/AVC, Dirac) EE 5359-Multimedia Processing- Spring 2012 Dr. K.R Rao By: Sumedha Phatak(1000731131) Objective A study, implementation and comparison

More information

Study and Implementation of Video Compression Standards (H.264/AVC and Dirac)

Study and Implementation of Video Compression Standards (H.264/AVC and Dirac) Project Proposal Study and Implementation of Video Compression Standards (H.264/AVC and Dirac) Sumedha Phatak-1000731131- sumedha.phatak@mavs.uta.edu Objective: A study, implementation and comparison of

More information

Understanding Compression Technologies for HD and Megapixel Surveillance

Understanding Compression Technologies for HD and Megapixel Surveillance When the security industry began the transition from using VHS tapes to hard disks for video surveillance storage, the question of how to compress and store video became a top consideration for video surveillance

More information

Parallelization of video compressing with FFmpeg and OpenMP in supercomputing environment

Parallelization of video compressing with FFmpeg and OpenMP in supercomputing environment Proceedings of the 9 th International Conference on Applied Informatics Eger, Hungary, January 29 February 1, 2014. Vol. 1. pp. 231 237 doi: 10.14794/ICAI.9.2014.1.231 Parallelization of video compressing

More information

Efficient Motion Estimation by Fast Three Step Search Algorithms

Efficient Motion Estimation by Fast Three Step Search Algorithms Efficient Motion Estimation by Fast Three Step Search Algorithms Namrata Verma 1, Tejeshwari Sahu 2, Pallavi Sahu 3 Assistant professor, Dept. of Electronics & Telecommunication Engineering, BIT Raipur,

More information

A Scalable Video-on-Demand Service for the Provision of VCR-Like Functions 1

A Scalable Video-on-Demand Service for the Provision of VCR-Like Functions 1 A Scalable Video-on-Demand Service for the Provision of VCR-Like Functions H.J. Chen, A. Krishnamurthy, T.D.C. Little, and D. Venkatesh, Boston University Multimedia Communications Laboratory Department

More information

Performance Analysis and Comparison of JM 15.1 and Intel IPP H.264 Encoder and Decoder

Performance Analysis and Comparison of JM 15.1 and Intel IPP H.264 Encoder and Decoder Performance Analysis and Comparison of 15.1 and H.264 Encoder and Decoder K.V.Suchethan Swaroop and K.R.Rao, IEEE Fellow Department of Electrical Engineering, University of Texas at Arlington Arlington,

More information

Figure 1: Relation between codec, data containers and compression algorithms.

Figure 1: Relation between codec, data containers and compression algorithms. Video Compression Djordje Mitrovic University of Edinburgh This document deals with the issues of video compression. The algorithm, which is used by the MPEG standards, will be elucidated upon in order

More information

White paper. H.264 video compression standard. New possibilities within video surveillance.

White paper. H.264 video compression standard. New possibilities within video surveillance. White paper H.264 video compression standard. New possibilities within video surveillance. Table of contents 1. Introduction 3 2. Development of H.264 3 3. How video compression works 4 4. H.264 profiles

More information

Internet Video Streaming and Cloud-based Multimedia Applications. Outline

Internet Video Streaming and Cloud-based Multimedia Applications. Outline Internet Video Streaming and Cloud-based Multimedia Applications Yifeng He, yhe@ee.ryerson.ca Ling Guan, lguan@ee.ryerson.ca 1 Outline Internet video streaming Overview Video coding Approaches for video

More information

A Look at Emerging Standards in Video Security Systems. Chris Adesanya Panasonic Network Systems Company Chris.Adesanya@us.panasonic.

A Look at Emerging Standards in Video Security Systems. Chris Adesanya Panasonic Network Systems Company Chris.Adesanya@us.panasonic. A Look at Emerging Standards in Video Security Systems Chris Adesanya Panasonic Network Systems Company Chris.Adesanya@us.panasonic.com Standards Standards are published documents that establish specifications

More information

A Prediction-Based Transcoding System for Video Conference in Cloud Computing

A Prediction-Based Transcoding System for Video Conference in Cloud Computing A Prediction-Based Transcoding System for Video Conference in Cloud Computing Yongquan Chen 1 Abstract. We design a transcoding system that can provide dynamic transcoding services for various types of

More information

CLOUDDMSS: CLOUD-BASED DISTRIBUTED MULTIMEDIA STREAMING SERVICE SYSTEM FOR HETEROGENEOUS DEVICES

CLOUDDMSS: CLOUD-BASED DISTRIBUTED MULTIMEDIA STREAMING SERVICE SYSTEM FOR HETEROGENEOUS DEVICES CLOUDDMSS: CLOUD-BASED DISTRIBUTED MULTIMEDIA STREAMING SERVICE SYSTEM FOR HETEROGENEOUS DEVICES 1 MYOUNGJIN KIM, 2 CUI YUN, 3 SEUNGHO HAN, 4 HANKU LEE 1,2,3,4 Department of Internet & Multimedia Engineering,

More information

Understanding Network Video Security Systems

Understanding Network Video Security Systems Understanding Network Video Security Systems Chris Adesanya Panasonic System Solutions Company adesanyac@us.panasonic.com Introduction and Overview This session will provide vendor neutral introduction

More information

Transmitting Video Images in XML Web Service

Transmitting Video Images in XML Web Service Transmitting Video Images in XML Web Service Francisco Prieto, Antonio J. Sierra, María Carrión García Departamento de Ingeniería de Sistemas y Automática Área de Ingeniería Telemática Escuela Superior

More information

Parallel Compression and Decompression of DNA Sequence Reads in FASTQ Format

Parallel Compression and Decompression of DNA Sequence Reads in FASTQ Format , pp.91-100 http://dx.doi.org/10.14257/ijhit.2014.7.4.09 Parallel Compression and Decompression of DNA Sequence Reads in FASTQ Format Jingjing Zheng 1,* and Ting Wang 1, 2 1,* Parallel Software and Computational

More information

Mobile Virtual Network Computing System

Mobile Virtual Network Computing System Mobile Virtual Network Computing System Vidhi S. Patel, Darshi R. Somaiya Student, Dept. of I.T., K.J. Somaiya College of Engineering and Information Technology, Mumbai, India ABSTRACT: we are planning

More information

Implementation of H.264 Video Codec for Block Matching Algorithms

Implementation of H.264 Video Codec for Block Matching Algorithms Implementation of H.264 Video Codec for Block Matching Algorithms Vivek Sinha 1, Dr. K. S. Geetha 2 1 Student of Master of Technology, Communication Systems, Department of ECE, R.V. College of Engineering,

More information

Load Balancing in Fault Tolerant Video Server

Load Balancing in Fault Tolerant Video Server Load Balancing in Fault Tolerant Video Server # D. N. Sujatha*, Girish K*, Rashmi B*, Venugopal K. R*, L. M. Patnaik** *Department of Computer Science and Engineering University Visvesvaraya College of

More information

Video compression: Performance of available codec software

Video compression: Performance of available codec software Video compression: Performance of available codec software Introduction. Digital Video A digital video is a collection of images presented sequentially to produce the effect of continuous motion. It takes

More information

Scalability and Classifications

Scalability and Classifications Scalability and Classifications 1 Types of Parallel Computers MIMD and SIMD classifications shared and distributed memory multicomputers distributed shared memory computers 2 Network Topologies static

More information

Motion Estimation. Macroblock Partitions. Sub-pixel Motion Estimation. Sub-pixel Motion Estimation

Motion Estimation. Macroblock Partitions. Sub-pixel Motion Estimation. Sub-pixel Motion Estimation Motion Estimation Motion Estimation and Intra Frame Prediction in H.264/AVC Encoder Rahul Vanam University of Washington H.264/AVC Encoder [2] 2 Motion Estimation H.264 does block based coding. Each frame

More information

H 261. Video Compression 1: H 261 Multimedia Systems (Module 4 Lesson 2) H 261 Coding Basics. Sources: Summary:

H 261. Video Compression 1: H 261 Multimedia Systems (Module 4 Lesson 2) H 261 Coding Basics. Sources: Summary: Video Compression : 6 Multimedia Systems (Module Lesson ) Summary: 6 Coding Compress color motion video into a low-rate bit stream at following resolutions: QCIF (76 x ) CIF ( x 88) Inter and Intra Frame

More information

Understanding Megapixel Camera Technology for Network Video Surveillance Systems. Glenn Adair

Understanding Megapixel Camera Technology for Network Video Surveillance Systems. Glenn Adair Understanding Megapixel Camera Technology for Network Video Surveillance Systems Glenn Adair Introduction (1) 3 MP Camera Covers an Area 9X as Large as (1) VGA Camera Megapixel = Reduce Cameras 3 Mega

More information

REPRESENTATION, CODING AND INTERACTIVE RENDERING OF HIGH- RESOLUTION PANORAMIC IMAGES AND VIDEO USING MPEG-4

REPRESENTATION, CODING AND INTERACTIVE RENDERING OF HIGH- RESOLUTION PANORAMIC IMAGES AND VIDEO USING MPEG-4 REPRESENTATION, CODING AND INTERACTIVE RENDERING OF HIGH- RESOLUTION PANORAMIC IMAGES AND VIDEO USING MPEG-4 S. Heymann, A. Smolic, K. Mueller, Y. Guo, J. Rurainsky, P. Eisert, T. Wiegand Fraunhofer Institute

More information

Parametric Comparison of H.264 with Existing Video Standards

Parametric Comparison of H.264 with Existing Video Standards Parametric Comparison of H.264 with Existing Video Standards Sumit Bhardwaj Department of Electronics and Communication Engineering Amity School of Engineering, Noida, Uttar Pradesh,INDIA Jyoti Bhardwaj

More information

Bandwidth Adaptation for MPEG-4 Video Streaming over the Internet

Bandwidth Adaptation for MPEG-4 Video Streaming over the Internet DICTA2002: Digital Image Computing Techniques and Applications, 21--22 January 2002, Melbourne, Australia Bandwidth Adaptation for MPEG-4 Video Streaming over the Internet K. Ramkishor James. P. Mammen

More information

Parallel Ray Tracing using MPI: A Dynamic Load-balancing Approach

Parallel Ray Tracing using MPI: A Dynamic Load-balancing Approach Parallel Ray Tracing using MPI: A Dynamic Load-balancing Approach S. M. Ashraful Kadir 1 and Tazrian Khan 2 1 Scientific Computing, Royal Institute of Technology (KTH), Stockholm, Sweden smakadir@csc.kth.se,

More information

A Simultaneous Solution for General Linear Equations on a Ring or Hierarchical Cluster

A Simultaneous Solution for General Linear Equations on a Ring or Hierarchical Cluster Acta Technica Jaurinensis Vol. 3. No. 1. 010 A Simultaneous Solution for General Linear Equations on a Ring or Hierarchical Cluster G. Molnárka, N. Varjasi Széchenyi István University Győr, Hungary, H-906

More information

A Performance Study of Load Balancing Strategies for Approximate String Matching on an MPI Heterogeneous System Environment

A Performance Study of Load Balancing Strategies for Approximate String Matching on an MPI Heterogeneous System Environment A Performance Study of Load Balancing Strategies for Approximate String Matching on an MPI Heterogeneous System Environment Panagiotis D. Michailidis and Konstantinos G. Margaritis Parallel and Distributed

More information

White paper. An explanation of video compression techniques.

White paper. An explanation of video compression techniques. White paper An explanation of video compression techniques. Table of contents 1. Introduction to compression techniques 4 2. Standardization organizations 4 3. Two basic standards: JPEG and MPEG 4 4. The

More information

Network Disk Recorder WJ-ND200

Network Disk Recorder WJ-ND200 Network Disk Recorder WJ-ND200 Network Disk Recorder Operating Instructions Model No. WJ-ND200 ERROR MIRROR TIMER HDD1 REC LINK /ACT OPERATE HDD2 ALARM SUSPEND ALARM BUZZER STOP Before attempting to connect

More information

High Quality Region-of-Interest Coding for Video Conferencing based Remote General Practitioner Training

High Quality Region-of-Interest Coding for Video Conferencing based Remote General Practitioner Training etelemed 13 : The Fifth International Conference on ehealth, Telemedicine, and Social Medicine High Quality Region-of-Interest Coding for Video Conferencing based Remote General Practitioner Training Manzur

More information

Complexity-rate-distortion Evaluation of Video Encoding for Cloud Media Computing

Complexity-rate-distortion Evaluation of Video Encoding for Cloud Media Computing Complexity-rate-distortion Evaluation of Video Encoding for Cloud Media Computing Ming Yang, Jianfei Cai, Yonggang Wen and Chuan Heng Foh School of Computer Engineering, Nanyang Technological University,

More information

Efficient Coding Unit and Prediction Unit Decision Algorithm for Multiview Video Coding

Efficient Coding Unit and Prediction Unit Decision Algorithm for Multiview Video Coding JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY, VOL. 13, NO. 2, JUNE 2015 97 Efficient Coding Unit and Prediction Unit Decision Algorithm for Multiview Video Coding Wei-Hsiang Chang, Mei-Juan Chen, Gwo-Long

More information

A General Framework for Tracking Objects in a Multi-Camera Environment

A General Framework for Tracking Objects in a Multi-Camera Environment A General Framework for Tracking Objects in a Multi-Camera Environment Karlene Nguyen, Gavin Yeung, Soheil Ghiasi, Majid Sarrafzadeh {karlene, gavin, soheil, majid}@cs.ucla.edu Abstract We present a framework

More information

Multidimensional Transcoding for Adaptive Video Streaming

Multidimensional Transcoding for Adaptive Video Streaming Multidimensional Transcoding for Adaptive Video Streaming Jens Brandt, Lars Wolf Institut für Betriebssystem und Rechnerverbund Technische Universität Braunschweig Germany NOSSDAV 2007, June 4-5 Jens Brandt,

More information

Self-Compressive Approach for Distributed System Monitoring

Self-Compressive Approach for Distributed System Monitoring Self-Compressive Approach for Distributed System Monitoring Akshada T Bhondave Dr D.Y Patil COE Computer Department, Pune University, India Santoshkumar Biradar Assistant Prof. Dr D.Y Patil COE, Computer

More information

A Mathematical Model for Evaluating the Perceptual Quality of Video

A Mathematical Model for Evaluating the Perceptual Quality of Video A Mathematical Model for Evaluating the Perceptual Quality of Video Jose Joskowicz, José-Carlos López-Ardao, Miguel A. González Ortega, and Cándido López García ETSE Telecomunicación, Campus Universitario,

More information

Module 8 VIDEO CODING STANDARDS. Version 2 ECE IIT, Kharagpur

Module 8 VIDEO CODING STANDARDS. Version 2 ECE IIT, Kharagpur Module 8 VIDEO CODING STANDARDS Version ECE IIT, Kharagpur Lesson H. andh.3 Standards Version ECE IIT, Kharagpur Lesson Objectives At the end of this lesson the students should be able to :. State the

More information

X264: A HIGH PERFORMANCE H.264/AVC ENCODER. Loren Merritt and Rahul Vanam*

X264: A HIGH PERFORMANCE H.264/AVC ENCODER. Loren Merritt and Rahul Vanam* X264: A HIGH PERFORMANCE H.264/AVC ENCODER Loren Merritt and Rahul Vanam* In Preparation *Dept. of Electrical Engineering, University of Washington, Seattle, WA 98195-2500 Email: {lorenm, rahulv}@u.washington.edu

More information

Quality Estimation for Scalable Video Codec. Presented by Ann Ukhanova (DTU Fotonik, Denmark) Kashaf Mazhar (KTH, Sweden)

Quality Estimation for Scalable Video Codec. Presented by Ann Ukhanova (DTU Fotonik, Denmark) Kashaf Mazhar (KTH, Sweden) Quality Estimation for Scalable Video Codec Presented by Ann Ukhanova (DTU Fotonik, Denmark) Kashaf Mazhar (KTH, Sweden) Purpose of scalable video coding Multiple video streams are needed for heterogeneous

More information

A Novel Load Balancing Optimization Algorithm Based on Peer-to-Peer

A Novel Load Balancing Optimization Algorithm Based on Peer-to-Peer A Novel Load Balancing Optimization Algorithm Based on Peer-to-Peer Technology in Streaming Media College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China shuwanneng@yahoo.com.cn

More information

Video Authentication for H.264/AVC using Digital Signature Standard and Secure Hash Algorithm

Video Authentication for H.264/AVC using Digital Signature Standard and Secure Hash Algorithm Video Authentication for H.264/AVC using Digital Signature Standard and Secure Hash Algorithm Nandakishore Ramaswamy Qualcomm Inc 5775 Morehouse Dr, Sam Diego, CA 92122. USA nandakishore@qualcomm.com K.

More information

Master in Science, specialty in Information Systems. ITESM. México, June 1984.

Master in Science, specialty in Information Systems. ITESM. México, June 1984. LAURA CRUZ REYES Instituto Tecnológico de Cd. Madero División de Estudios de Posgrado e Investigación Juventino Rosas y Jesús Urueta Cd. Madero Tamaulipas México CP. 89440 Tel/Fax (52) 833 215 85 44 e-mail:

More information

The International Journal Of Science & Technoledge (ISSN 2321 919X) www.theijst.com

The International Journal Of Science & Technoledge (ISSN 2321 919X) www.theijst.com THE INTERNATIONAL JOURNAL OF SCIENCE & TECHNOLEDGE Efficient Parallel Processing on Public Cloud Servers using Load Balancing Manjunath K. C. M.Tech IV Sem, Department of CSE, SEA College of Engineering

More information

302 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 19, NO. 2, FEBRUARY 2009

302 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 19, NO. 2, FEBRUARY 2009 302 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 19, NO. 2, FEBRUARY 2009 Transactions Letters Fast Inter-Mode Decision in an H.264/AVC Encoder Using Mode and Lagrangian Cost Correlation

More information

Stream Processing on GPUs Using Distributed Multimedia Middleware

Stream Processing on GPUs Using Distributed Multimedia Middleware Stream Processing on GPUs Using Distributed Multimedia Middleware Michael Repplinger 1,2, and Philipp Slusallek 1,2 1 Computer Graphics Lab, Saarland University, Saarbrücken, Germany 2 German Research

More information

Load Balancing using Potential Functions for Hierarchical Topologies

Load Balancing using Potential Functions for Hierarchical Topologies Acta Technica Jaurinensis Vol. 4. No. 4. 2012 Load Balancing using Potential Functions for Hierarchical Topologies Molnárka Győző, Varjasi Norbert Széchenyi István University Dept. of Machine Design and

More information

DECENTRALIZED LOAD BALANCING IN HETEROGENEOUS SYSTEMS USING DIFFUSION APPROACH

DECENTRALIZED LOAD BALANCING IN HETEROGENEOUS SYSTEMS USING DIFFUSION APPROACH DECENTRALIZED LOAD BALANCING IN HETEROGENEOUS SYSTEMS USING DIFFUSION APPROACH P.Neelakantan Department of Computer Science & Engineering, SVCET, Chittoor pneelakantan@rediffmail.com ABSTRACT The grid

More information

Study Element Based Adaptation of Lecture Videos to Mobile Devices

Study Element Based Adaptation of Lecture Videos to Mobile Devices Study Element Based Adaptation of Lecture Videos to Mobile Devices Ganesh Narayana Murthy Department of Computer Science and Engineering Indian Institute of Technology Bombay Powai, Mumbai - 476 Email:

More information

SCALABLE DISTRIBUTED VIDEO TRANSCODING ARCHITECTURE. Tewodros Deneke

SCALABLE DISTRIBUTED VIDEO TRANSCODING ARCHITECTURE. Tewodros Deneke SCALABLE DISTRIBUTED VIDEO TRANSCODING ARCHITECTURE Tewodros Deneke Master of Science Thesis Supervisor: Prof. Johan Lilius Advisor: Dr. Sébastien Lafond Embedded Systems Laboratory Department of Information

More information

Enlarge Bandwidth of Multimedia Server with Network Attached Storage System

Enlarge Bandwidth of Multimedia Server with Network Attached Storage System Enlarge Bandwidth of Multimedia Server with Network Attached Storage System Dan Feng, Yuhui Deng, Ke Zhou, Fang Wang Key Laboratory of Data Storage System, Ministry of Education College of Computer, Huazhong

More information

Cellular Computing on a Linux Cluster

Cellular Computing on a Linux Cluster Cellular Computing on a Linux Cluster Alexei Agueev, Bernd Däne, Wolfgang Fengler TU Ilmenau, Department of Computer Architecture Topics 1. Cellular Computing 2. The Experiment 3. Experimental Results

More information

Lossless Grey-scale Image Compression using Source Symbols Reduction and Huffman Coding

Lossless Grey-scale Image Compression using Source Symbols Reduction and Huffman Coding Lossless Grey-scale Image Compression using Source Symbols Reduction and Huffman Coding C. SARAVANAN cs@cc.nitdgp.ac.in Assistant Professor, Computer Centre, National Institute of Technology, Durgapur,WestBengal,

More information

Gaming as a Service. Prof. Victor C.M. Leung. The University of British Columbia, Canada www.ece.ubc.ca/~vleung

Gaming as a Service. Prof. Victor C.M. Leung. The University of British Columbia, Canada www.ece.ubc.ca/~vleung Gaming as a Service Prof. Victor C.M. Leung The University of British Columbia, Canada www.ece.ubc.ca/~vleung International Conference on Computing, Networking and Communications 4 February, 2014 Outline

More information

Journal of Theoretical and Applied Information Technology 20 th July 2015. Vol.77. No.2 2005-2015 JATIT & LLS. All rights reserved.

Journal of Theoretical and Applied Information Technology 20 th July 2015. Vol.77. No.2 2005-2015 JATIT & LLS. All rights reserved. EFFICIENT LOAD BALANCING USING ANT COLONY OPTIMIZATION MOHAMMAD H. NADIMI-SHAHRAKI, ELNAZ SHAFIGH FARD, FARAMARZ SAFI Department of Computer Engineering, Najafabad branch, Islamic Azad University, Najafabad,

More information

A Survey on Load Balancing and Scheduling in Cloud Computing

A Survey on Load Balancing and Scheduling in Cloud Computing IJIRST International Journal for Innovative Research in Science & Technology Volume 1 Issue 7 December 2014 ISSN (online): 2349-6010 A Survey on Load Balancing and Scheduling in Cloud Computing Niraj Patel

More information

A Software Radio Modem Approach Based on a DSP.

A Software Radio Modem Approach Based on a DSP. 245 Encuentro de Investigación en Ingeniería Eléctrica Zacatecas, Zac, Marzo 17 18, 2005 A Software Radio Modem Approach Based on a DSP. J. González Villarruel, Profesor Investigador, Escuela de Graduados

More information

Systems with Central Monitoring and Control for Data Center Infrastructure and the Effect of Video Frames in the Transmission Bandwidth

Systems with Central Monitoring and Control for Data Center Infrastructure and the Effect of Video Frames in the Transmission Bandwidth International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: 01 117 Systems with Central Monitoring and Control for Data Center Infrastructure and the Effect of Video Frames in the Transmission

More information

Evaluation of performance and complexity comparison for coding standards HEVC vs. H.264/AVC

Evaluation of performance and complexity comparison for coding standards HEVC vs. H.264/AVC Evaluation of performance and complexity comparison for coding standards HEVC vs. H.264/AVC Zoran M. Milicevic and Zoran S. Bojkovic Abstract In order to compare the performance and complexity without

More information

IDENTIFYING AND OPTIMIZING DATA DUPLICATION BY EFFICIENT MEMORY ALLOCATION IN REPOSITORY BY SINGLE INSTANCE STORAGE

IDENTIFYING AND OPTIMIZING DATA DUPLICATION BY EFFICIENT MEMORY ALLOCATION IN REPOSITORY BY SINGLE INSTANCE STORAGE IDENTIFYING AND OPTIMIZING DATA DUPLICATION BY EFFICIENT MEMORY ALLOCATION IN REPOSITORY BY SINGLE INSTANCE STORAGE 1 M.PRADEEP RAJA, 2 R.C SANTHOSH KUMAR, 3 P.KIRUTHIGA, 4 V. LOGESHWARI 1,2,3 Student,

More information

Introduction to image coding

Introduction to image coding Introduction to image coding Image coding aims at reducing amount of data required for image representation, storage or transmission. This is achieved by removing redundant data from an image, i.e. by

More information

MPEG-4 AVC/H.264 Video Codecs Comparison

MPEG-4 AVC/H.264 Video Codecs Comparison MPEG-4 AVC/H.264 Video Codecs Comparison Codecs: Video group head: Dr. Dmitriy Vatolin Project head: Dr. Dmitriy Kulikov Measurements, analysis: Alexander Parshin, Marat Arsaev Report correction: Alexander

More information

Performance Modeling and Analysis of a Database Server with Write-Heavy Workload

Performance Modeling and Analysis of a Database Server with Write-Heavy Workload Performance Modeling and Analysis of a Database Server with Write-Heavy Workload Manfred Dellkrantz, Maria Kihl 2, and Anders Robertsson Department of Automatic Control, Lund University 2 Department of

More information

Introduction to Cloud Computing

Introduction to Cloud Computing Introduction to Cloud Computing Parallel Processing I 15 319, spring 2010 7 th Lecture, Feb 2 nd Majd F. Sakr Lecture Motivation Concurrency and why? Different flavors of parallel computing Get the basic

More information

Multiple Description Coding (MDC) and Scalable Coding (SC) for Multimedia

Multiple Description Coding (MDC) and Scalable Coding (SC) for Multimedia Multiple Description Coding (MDC) and Scalable Coding (SC) for Multimedia Gürkan Gür PhD. Candidate e-mail: gurgurka@boun.edu.tr Dept. Of Computer Eng. Boğaziçi University Istanbul/TR ( Currenty@UNITN)

More information

Video Encryption Exploiting Non-Standard 3D Data Arrangements. Stefan A. Kramatsch, Herbert Stögner, and Andreas Uhl uhl@cosy.sbg.ac.

Video Encryption Exploiting Non-Standard 3D Data Arrangements. Stefan A. Kramatsch, Herbert Stögner, and Andreas Uhl uhl@cosy.sbg.ac. Video Encryption Exploiting Non-Standard 3D Data Arrangements Stefan A. Kramatsch, Herbert Stögner, and Andreas Uhl uhl@cosy.sbg.ac.at Andreas Uhl 1 Carinthia Tech Institute & Salzburg University Outline

More information

Remote Graphical Visualization of Large Interactive Spatial Data

Remote Graphical Visualization of Large Interactive Spatial Data Remote Graphical Visualization of Large Interactive Spatial Data ComplexHPC Spring School 2011 International ComplexHPC Challenge Cristinel Mihai Mocan Computer Science Department Technical University

More information

The Three-level Approaches for Differentiated Service in Clustering Web Server

The Three-level Approaches for Differentiated Service in Clustering Web Server The Three-level Approaches for Differentiated Service in Clustering Web Server Myung-Sub Lee and Chang-Hyeon Park School of Computer Science and Electrical Engineering, Yeungnam University Kyungsan, Kyungbuk

More information

QOS Requirements and Service Level Agreements. LECTURE 4 Lecturer: Associate Professor A.S. Eremenko

QOS Requirements and Service Level Agreements. LECTURE 4 Lecturer: Associate Professor A.S. Eremenko QOS Requirements and Service Level Agreements LECTURE 4 Lecturer: Associate Professor A.S. Eremenko Application SLA Requirements Different applications have different SLA requirements; the impact that

More information

MPEG-4 Video Transfer with SCTP-Friendly Rate Control Mohamed N. El Derini

MPEG-4 Video Transfer with SCTP-Friendly Rate Control Mohamed N. El Derini MPEG-4 Video Transfer with SCTP-Friendly Rate Control Mohamed N. El Derini elderini@ieee.org Amr A.Elshikh elshikha@emro.who.int Faculty of Engineering, Alexandria University, Egypt Computer Science and

More information

Application Note. Introduction. Video Basics. Contents. IP Video Encoding Explained Series Understanding IP Video Performance.

Application Note. Introduction. Video Basics. Contents. IP Video Encoding Explained Series Understanding IP Video Performance. Title Overview IP Video Encoding Explained Series Understanding IP Video Performance Date September 2012 (orig. May 2008) IP networks are increasingly used to deliver video services for entertainment,

More information

For Articulation Purpose Only

For Articulation Purpose Only E305 Digital Audio and Video (4 Modular Credits) This document addresses the content related abilities, with reference to the module. Abilities of thinking, learning, problem solving, team work, communication,

More information

Dependable, Open and Real-time Architecture for Power Systems

Dependable, Open and Real-time Architecture for Power Systems Dependable, Open and Real-time Architecture for Power Systems 48 Dependable, Open and Real-time Architecture for Power Systems DORA-Power Atsuhiko Hirota Masayuki Kikuchi Takashi Owaki Yukiyoshi Tani OVERVIEW:

More information

System-Level Display Power Reduction Technologies for Portable Computing and Communications Devices

System-Level Display Power Reduction Technologies for Portable Computing and Communications Devices System-Level Display Power Reduction Technologies for Portable Computing and Communications Devices Achintya K. Bhowmik and Robert J. Brennan Intel Corporation 2200 Mission College Blvd. Santa Clara, CA

More information

Load balancing in a heterogeneous computer system by self-organizing Kohonen network

Load balancing in a heterogeneous computer system by self-organizing Kohonen network Bull. Nov. Comp. Center, Comp. Science, 25 (2006), 69 74 c 2006 NCC Publisher Load balancing in a heterogeneous computer system by self-organizing Kohonen network Mikhail S. Tarkov, Yakov S. Bezrukov Abstract.

More information

Impact of Control Theory on QoS Adaptation in Distributed Middleware Systems

Impact of Control Theory on QoS Adaptation in Distributed Middleware Systems Impact of Control Theory on QoS Adaptation in Distributed Middleware Systems Baochun Li Electrical and Computer Engineering University of Toronto bli@eecg.toronto.edu Klara Nahrstedt Department of Computer

More information

A Dynamic Resource Management with Energy Saving Mechanism for Supporting Cloud Computing

A Dynamic Resource Management with Energy Saving Mechanism for Supporting Cloud Computing A Dynamic Resource Management with Energy Saving Mechanism for Supporting Cloud Computing Liang-Teh Lee, Kang-Yuan Liu, Hui-Yang Huang and Chia-Ying Tseng Department of Computer Science and Engineering,

More information

Real-Time DMB Video Encryption in Recording on PMP

Real-Time DMB Video Encryption in Recording on PMP Real-Time DMB Video Encryption in Recording on PMP Seong-Yeon Lee and Jong-Nam Kim Dept. of Electronic Computer Telecommunication Engineering, PuKyong Nat'l Univ. sylee9997@pknu.ac.kr, jongnam@pknu.ac.kr

More information

Advances in Smart Systems Research : ISSN 2050-8662 : http://nimbusvault.net/publications/koala/assr/ Vol. 3. No. 3 : pp.

Advances in Smart Systems Research : ISSN 2050-8662 : http://nimbusvault.net/publications/koala/assr/ Vol. 3. No. 3 : pp. Advances in Smart Systems Research : ISSN 2050-8662 : http://nimbusvault.net/publications/koala/assr/ Vol. 3. No. 3 : pp.49-54 : isrp13-005 Optimized Communications on Cloud Computer Processor by Using

More information

Development of Software Dispatcher Based. for Heterogeneous. Cluster Based Web Systems

Development of Software Dispatcher Based. for Heterogeneous. Cluster Based Web Systems ISSN: 0974-3308, VO L. 5, NO. 2, DECEMBER 2012 @ SRIMC A 105 Development of Software Dispatcher Based B Load Balancing AlgorithmsA for Heterogeneous Cluster Based Web Systems S Prof. Gautam J. Kamani,

More information

An Interactive Visualization Tool for the Analysis of Multi-Objective Embedded Systems Design Space Exploration

An Interactive Visualization Tool for the Analysis of Multi-Objective Embedded Systems Design Space Exploration An Interactive Visualization Tool for the Analysis of Multi-Objective Embedded Systems Design Space Exploration Toktam Taghavi, Andy D. Pimentel Computer Systems Architecture Group, Informatics Institute

More information

BSPCloud: A Hybrid Programming Library for Cloud Computing *

BSPCloud: A Hybrid Programming Library for Cloud Computing * BSPCloud: A Hybrid Programming Library for Cloud Computing * Xiaodong Liu, Weiqin Tong and Yan Hou Department of Computer Engineering and Science Shanghai University, Shanghai, China liuxiaodongxht@qq.com,

More information

Maximizing the number of users in an interactive video-ondemand. Citation Ieee Transactions On Broadcasting, 2002, v. 48 n. 4, p.

Maximizing the number of users in an interactive video-ondemand. Citation Ieee Transactions On Broadcasting, 2002, v. 48 n. 4, p. Title Maximizing the number of users in an interactive video-ondemand system Author(s) Bakiras, S; Li, VOK Citation Ieee Transactions On Broadcasting, 2002, v. 48 n. 4, p. 281-292 Issued Date 2002 URL

More information

http://www.springer.com/0-387-23402-0

http://www.springer.com/0-387-23402-0 http://www.springer.com/0-387-23402-0 Chapter 2 VISUAL DATA FORMATS 1. Image and Video Data Digital visual data is usually organised in rectangular arrays denoted as frames, the elements of these arrays

More information

ARTICLE. 10 reasons to switch to IP-based video

ARTICLE. 10 reasons to switch to IP-based video ARTICLE 10 reasons to switch to IP-based video Table of contents 1. High resolution 3 2. Easy to install 4 3. Truly digital 5 4. Camera intelligence 5 5. Fully integrated 7 6. Built-in security 7 7. Crystal-clear

More information

Effective Load Balancing Based on Cloud Partitioning for the Public Cloud

Effective Load Balancing Based on Cloud Partitioning for the Public Cloud Effective Load Balancing Based on Cloud Partitioning for the Public Cloud 1 T.Satya Nagamani, 2 D.Suseela Sagar 1,2 Dept. of IT, Sir C R Reddy College of Engineering, Eluru, AP, India Abstract Load balancing

More information

Middleware and Distributed Systems. Introduction. Dr. Martin v. Löwis

Middleware and Distributed Systems. Introduction. Dr. Martin v. Löwis Middleware and Distributed Systems Introduction Dr. Martin v. Löwis 14 3. Software Engineering What is Middleware? Bauer et al. Software Engineering, Report on a conference sponsored by the NATO SCIENCE

More information

Dynamic and Adaptive Organization of Data-Collection Infrastructures in Sustainable Wireless Sensor Networks

Dynamic and Adaptive Organization of Data-Collection Infrastructures in Sustainable Wireless Sensor Networks 928 Dynamic and Adaptive Organization of Data-Collection Infrastructures in Sustainable Wireless Sensor Networks Rui Teng, Shirazi N. Mehdad and Bing Zhang National institute of information and communications

More information

Efficient DNS based Load Balancing for Bursty Web Application Traffic

Efficient DNS based Load Balancing for Bursty Web Application Traffic ISSN Volume 1, No.1, September October 2012 International Journal of Science the and Internet. Applied However, Information this trend leads Technology to sudden burst of Available Online at http://warse.org/pdfs/ijmcis01112012.pdf

More information

Journal of Chemical and Pharmaceutical Research, 2013, 5(12):118-122. Research Article. An independence display platform using multiple media streams

Journal of Chemical and Pharmaceutical Research, 2013, 5(12):118-122. Research Article. An independence display platform using multiple media streams Available online www.jocpr.com Journal of Chemical and Pharmaceutical Research, 2013, 5(12):118-122 Research Article ISSN : 0975-7384 CODEN(USA) : JCPRC5 An independence display platform using multiple

More information

Introduzione alle Biblioteche Digitali Audio/Video

Introduzione alle Biblioteche Digitali Audio/Video Introduzione alle Biblioteche Digitali Audio/Video Biblioteche Digitali 1 Gestione del video Perchè è importante poter gestire biblioteche digitali di audiovisivi Caratteristiche specifiche dell audio/video

More information

For More Information. Setting Bitrate Control and Keyframe Parameters

For More Information. Setting Bitrate Control and Keyframe Parameters Setting Bitrate Control and Keyframe Parameters Jan Ozer www.streaminglearningcenter.com @janozer jozer@mindspring.com/ 276-238-9135 For More Information! Most information derived from this book.! For

More information

Building an Inexpensive Parallel Computer

Building an Inexpensive Parallel Computer Res. Lett. Inf. Math. Sci., (2000) 1, 113-118 Available online at http://www.massey.ac.nz/~wwiims/rlims/ Building an Inexpensive Parallel Computer Lutz Grosz and Andre Barczak I.I.M.S., Massey University

More information

IP Video Rendering Basics

IP Video Rendering Basics CohuHD offers a broad line of High Definition network based cameras, positioning systems and VMS solutions designed for the performance requirements associated with critical infrastructure applications.

More information

Influence of Load Balancing on Quality of Real Time Data Transmission*

Influence of Load Balancing on Quality of Real Time Data Transmission* SERBIAN JOURNAL OF ELECTRICAL ENGINEERING Vol. 6, No. 3, December 2009, 515-524 UDK: 004.738.2 Influence of Load Balancing on Quality of Real Time Data Transmission* Nataša Maksić 1,a, Petar Knežević 2,

More information

Grid Video Processing

Grid Video Processing Grid Video Processing Distributed Approach to Video Processing Petr Holub hopet@ics.muni.cz CESNET z. s. p. o. Laboratory of Advanced Networking Technologies Institute of Computer Science and Faculty of

More information

IMPACT OF COMPRESSION ON THE VIDEO QUALITY

IMPACT OF COMPRESSION ON THE VIDEO QUALITY IMPACT OF COMPRESSION ON THE VIDEO QUALITY Miroslav UHRINA 1, Jan HLUBIK 1, Martin VACULIK 1 1 Department Department of Telecommunications and Multimedia, Faculty of Electrical Engineering, University

More information

Scalable Video Streaming in Wireless Mesh Networks for Education

Scalable Video Streaming in Wireless Mesh Networks for Education Scalable Video Streaming in Wireless Mesh Networks for Education LIU Yan WANG Xinheng LIU Caixing 1. School of Engineering, Swansea University, Swansea, UK 2. College of Informatics, South China Agricultural

More information