A poisson hidden markov model for multiview video traffic
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Nexgen Technology provides final year IEEE projects for 2015-2016. Contact information is provided for Praveen Kumar. The document then summarizes a project titled "A Poisson Hidden Markov Model for Multiview Video Traffic". It proposes using a Poisson hidden Markov model to characterize the variable bit-rate traffic generated by Multiview Video Coding. The model represents the video activity evolution in one layer and encoded frame sizes across multiple views in another layer. Extensive simulations show the model can accurately produce realistic traffic characteristics for MVC sequences and predict sender/receiver buffers and network load for interactive multiview services.
A poisson hidden markov model for multiview video traffic
- 1. NEXGEN TECHNOLOGY FINAL YEAR IEEE PROJECTS TITLES 2015-2016 CONTACT: PRAVEEN KUMAR. L (+91 – 9751442511, +91 – 9791938249) MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com web:www.nexgenproject.com 1. A Poisson Hidden Markov Model for Multiview Video Traffic Multiview video has recently emerged as a means to improve user experience in novel multimedia services. We propose a new stochastic model to characterize the traffic generated by a Multiview Video Coding (MVC) variable bit-rate source. To this aim, we resort to a Poisson hidden Markov model (P-HMM), in which the first (hidden) layer represents the evolution of the video activity and the second layer represents the frame sizes of the multiple encoded views. We propose a method for estimating the model parameters in long MVC sequences. We then present extensive numerical simulations assessing the model's ability to produce traffic with realistic characteristics for a general class of MVC sequences. We then extend our framework to network applications where we show that our model is able to accurately describe the sender and receiver buffers behavior in MVC transmission. Finally, we derive a model of user behavior for interactive view selection, which, in conjunction with our traffic model, is able to accurately predict actual network load in interactive multiview services. .