QUALITY OF EXPERIENCE METHODS AND MODELS FOR MULTI-SENSORIAL MEDIA

In this work, the evaluation of the Quality of Experience (QoE), i.e., “the degree of delight or annoyance of the user of an application or service” for multi-sensorial media has been investigated. An IoT based architecture for multi-sensorial media delivery to TV users in a home entertainment scenario proposed and the synchronization requirement between media and devices analyzed and the architecture of the system defined accordingly. Furthermore, a prototype is implemented in a real smart home scenario with real customary devices, which allowed a subjective test measurement campaign to assess the QoE of the users and the feasibility of the proposed multi-sensorial media TV service. The users’ perception of multi-sensorial media analyzed, in particular the influence of three sensory effects (i.e., airflow, vibration, and light) on user enjoyment, annoyance, emotions, and if the user like to experience additional sensory effects when viewing multi-sensory content. Furthermore investigate if multi-sensorial media can enhance the user sense of reality, as well the audiovisual content, and which effect has the highest impact on user experience for an IoT based multi-sensorial media in smart home. Finally a novel parametric model suitable for the estimation of the QoE for multi-sensorial media TV in smart home proposed. The proposed model can be used by TV service providers to predict the enhancement produced by adding the effects to conventional TV services and plan for delivery multi-sensorial media as new advanced TV service. The parameter estimation of the model relies on Particle Swarm Optimization (PSO) which has been successfully applied to the optimization of nonlinear problems. A comparative analysis of the performance and the models’ prediction accuracies of the proposed model with the state of the art model for the QoE have been carried out based on the same MOS dataset to assess the effectiveness of the former with respect to the latter.