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.

In a multiple access technologies scenario, aimed at providing the “always best connected” (ABC) service, the future fourth-generation wireless networks will be characterized by an increasing heterogeneity. When a multimedia transmission is considered, the ABC can be improved by the optimization of the source coding. In this work, we propose a joint source channel coding (JSCC) optimization technique, able to adapt the system parameters to channel variations, providing at the same time a high level of scalability of the encoded video bitstream. With the proposed JSCC method, we address the ABC issue in multi-user scenario affected by multiple interference. The results show that, despite the simplicity of the algorithm, the adaptive system experiences an improvement of the performance compared to fixed coding schemes.

In this paper, we address the challenging topic of target detection and tracking in underwater environments, proposing a behavior-based algorithm suitable for both fixed and moving targets. Taking into account the stringent constraints of the acoustic underwater channel, we apply this method to fleets of collaborative AUVs. The resulting control strategy is able to benefit from the behavior-based approach and the cooperation among robots. Results show that the performance of the algorithm can be improved by tuning the range of vehicles communication, meeting the tradeoff between energy consumption and behavior efficiency.

Multiview video is increasingly getting attention due to emerging applications such as 3DTV and immersive teleconferencing. In this paper, we present a non-stationary Hidden Markov Model (HMM) for characterizing the data rate of compressed multiview content. The states of the model correspond to different video activity levels and exhibit a Poisson state duration distribution. We derive a stable maximum likelihood algorithm for estimating the parameters of our multiview traffic model. Synthetic data generated by the model exhibits statistics that closely match those of actual multiview data. In addition, we demonstrate the high accuracy of the model in two multiview streaming applications by evaluating the frame loss rate of a constrained network buffer fed by actual and synthetic data.

In this paper we develop a strategy for visual relevancy evaluation using Rate Distortion analysis in the Circular Harmonic Functions (CHF) Domain. In the CHF domain, the different visual characteristics of the transformed frames - edges, lines, forks, crosses, etc - are emphasized. Resorting to this domain we analyze a new approach to evaluate the distortion due to an event of video data loss accounting for the visual relevance of individual lost data. In a real video streaming session, distortion measurements taking into account the visual impact of error events can be stored along with pre-coded video sequence so as to drive the strategies for ad-hoc error protection. Numerical simulations show that the approach herein proposed to evaluate the visual impact of loss events in the decoded sequences captures the visual distortion much better than commonly adopted mean square error criteria between the decoded and the original sequence.

A novel model-based steganographic technique for JPEG images is proposed where themodel, derived from heuristic assumptions about the shape of the DCT frequency histograms, is dependent on a stegokey. The secret message is embedded in DCT domain through an accurate selection of the potentially modifiable coefficients, taking into account their visual and statistical relevancy. A novel block measure, named discrepancy, is introduced in order to select suitable areas for embedding. The visual impact of the steganographic technique is evaluated through PSNR measures. State-of-the-art steganalytical test is also performed to offer a comparison with the original model-based techniques.