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Augmented reality is one of the enabling technologies of the upcoming future. Its usage in working and learning scenarios may lead to a better quality of work and training by helping the operators during the most crucial stages of processes. Therefore, the automatic detection of stress during augmented reality experiences can be a valuable support to prevent consequences on people's health and foster the spreading of this technology. In this work, we present the design of a non-invasive stress assessment approach. The proposed system is based on the analysis of the head movements of people wearing a Head Mounted Display while performing stress-inducing tasks. First, we designed a subjective experiment consisting of two stress-related tests for data acquisition. Then, a statistical analysis of head movements has been performed to determine which features are representative of the presence of stress. Finally, a stress classifier based on a combination of Support Vector Machines has been designed and trained. The proposed approach achieved promising performances thus paving the way for further studies in this research direction.

In the near future, the broadcasting scenario will be characterized by immersive content. One of the systems for capturing the 3D content of a scene is the Light Field imaging. The huge amount of data and the specific transmission scenario impose strong constraints on services and applications. Among others, the evaluation of the quality of the received media cannot rely on the original signal but should be based only on the received data. In this direction, we propose a no-reference quality metric for light field images which is based on spatial and angular characteristics. In more details, the estimated saliency and cyclopean maps of light field images are exploited to extract the spatial features. The angular consistency features are, instead, measured with the use of the Global Luminance Distribution knowledge and the Weighted Local Binary Patterns operator on Epipolar Plane Images. The effectiveness of the proposed metric is assessed by comparing its performance with state-of-the-art quality metrics using 4 datasets: SMART, Win5-LID, VALID 10-bit, and VALID 8-bit. Furthermore, the performance is analyzed in cross-datasets, with different distortions, and for different saliency maps. The achieved results show that the performance of the proposed model outperforms state-of-the-art approaches and perform well for different distortion types and with various saliency models.

'View plus depth' is an attractive compact representation format for 3D video compression and transmission. It combines 2D video with depth map sequence aligned in a per-pixel manner to represent the moving 3D scene in interest. Any different-perspective view can be synthesized out if this representation through Depth-Image Based Rendering (DIBR). However, such rendering is prone to disocclusion errors: regions originally covered by foreground objects become visible in the synthesized view and have to be filled with perceptually-meaningful data. In this work, a technique for reducing the perceived artifacts by inpainting the disoccluded areas is proposed. Based on Criminisi's exemplar-based inpainting algorithm, the developed technique recovers the disoccluded areas by using pixels of similar blocks surrounding it. In the original work, a moving window is centered on the boundaries between known and unknown parts ('target window'). The known pixels are used to select windows which are most similar to the target one. When this process is completed, the unknown region of the target patch is filled with a weighted combination of pixels from the selected windows. In the proposed scheme, the priority map, which defines the rule for selecting the order of pixels to be filled, has been modified to meet the requirement for disocclusion hole filling and a better non-local mean estimate has been suggested accordingly. Furthermore, the search for similar patches has also been extended to previous and following frames of the video under processing, thus improving both computational efficiency and resulting quality.