Neuro-Fuzzy Modeling and Prediction of VBR MPEG Video Sources |
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| Affiliation: | 1. School of Computer Science and Software Engineering, Monash University, Wellington Road, Clayton, Vic. 3168, Australia;2. Department of Computer Science, Tennessee State University, Nashville, TN, 37209-1561, USA;1. HEC Paris, 1 rue de la Liberation, 78351 Jouy-en-Josas Cedex, France;2. Research Institute of Industrial Economics (IFN), Box 55665, SE-102 15 Stockholm, Sweden;1. Institute of Clinical Pharmacology, Anhui Medical University, Key Laboratory of Anti-Inflammatory and Immune Medicine (Anhui Medical University), Ministry of Education, Anhui Collaborative Innovation Center of Anti-inflammatory and Immune Medicine, Hefei 230032, China;2. Public Health and Preventive Medicine Postdoctoral Research Station of Anhui Medical University, Hefei 230032, China;1. KU Leuven, Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, Leuven, Belgium;2. University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, the Netherlands;3. KU Leuven, Leuven Brain Institute (LBI), Leuven, Belgium;4. University of Groningen, University Medical Center Groningen, Department of Neurology, Groningen, the Netherlands;5. Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy;6. Institute of Sport Sciences and Physical Education, Faculty of Sciences, University of Pécs, Pécs, Hungary;7. Somogy County Kaposi Mór Teaching Hospital, Kaposvár, Hungary |
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| Abstract: | The work presented in this paper intends to apply neuro-fuzzy methods for the modeling and prediction on traffic intensity of digital video sources which are coded with hybrid Motion Compensation/Differential Pulse Code Modulation/Discrete Cosine Transform (MC/DPCM/DCT) algorithm. Although current coding standards recommend constant bit rate (CBR) output by means of a smoothing buffer, the hybrid algorithm inherently produces variable bit rate (VBR) output. This paper describes the novel application of a fuzzy predictor for the purposes of modeling and prediction on video sources. The computation requirement of the fuzzy predictor and its neural network implementation are also discussed. The proposed fuzzy prediction method and its neural network version can be applied to the development of connection admission control, usage parameter control and congestion control algorithms in ATM networks. |
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