Dr. Jacob Chakareski, assistant professor of electrical and computer engineering who recently joined The University of Alabama College of Engineering, is the author of one paper and co-author on another paper recently published in journals affiliated with IEEE.
The first paper is in the February 2014 issue of the IEEE Transactions on Image Processing, a publication of the IEEE Signal Processing Society. In the paper, titled “Transmission Policy Selection for Multi-View Content Delivery Over Bandwidth Constrained Channels,” Dr. Chakareski formulates an optimization framework for computing the transmission actions of streaming multi-view video content over bandwidth constrained channels.
The optimization finds the schedule for sending the packetized data that maximizes the reconstruction quality of the content, for the given network bandwidth. Two prospective multi-view content representation formats are considered: 1) MVC and 2) video plus depth. In the case of each, he formulates directed graph models that characterize the interdependencies between the data units that comprise the content. For the video plus depth format, Dr. Chakareski develops a novel space-time error concealment strategy that reconstructs the missing content based on received data units from multiple views. He designs multiple techniques to solve the optimization problem of interest, at varying degrees of complexity and accuracy. In conjunction, Dr. Chakareski derives spatiotemporal models of the reconstruction error for the multi-view content that he employs to reduce the computational requirements of the optimization. He studies the performance of his framework via simulation experiments. Significant gains in terms of rate-distortion efficiency are demonstrated over various reference methods.
The second paper is accepted for publication in the IEEE/ACM Transactions on Networking, but is already available on the publication’s website. Titled “A Poisson Hidden Markov Model for Multiview Video Traffic,” Dr. Chakareski and other co-authors propose a new stochastic model to characterize the traffic generated by a Multiview Video Coding (MVC) variable bit-rate source. Multiview video has recently emerged as a means to improve user experience in novel multimedia services.
To this aim, the researchers 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. They propose a method for estimating the model parameters in long MVC sequences. The researchers then present extensive numerical simulations assessing the model’s ability to produce traffic with realistic characteristics for a general class of MVC sequences.
They then extend their framework to network applications where they show that their model is able to accurately describe the sender and receiver buffers behavior in MVC transmission. Finally, Dr. Chakareski and the other co-authors derive a model of user behavior for interactive view selection, which, in conjunction with their trafficmodel, is able to accurately predict actual network load in interactive multiview services.