Pablo Pérez

Pablo Perez, Lucjan Janowski, Narciso Garcia et al.

Recent studies have shown that it is possible to characterize subject bias and variance in subjective assessment tests. Apparent differences among subjects can, for the most part, be explained by random factors. Building on that theory, we propose a subjective test design where three to four team members each rate the stimuli multiple times. The results are comparable to a high performing objective metric. This provides a quick and simple way to analyze new technologies and perform pre-tests for subjective assessment.

Marta Orduna, Pablo Pérez, Jesús Gutiérrez et al.

This paper analyzes the joint assessment of quality, spatial and social presence, empathy, attitude, and attention in three conditions: (A)visualizing and rating the quality of contents in a Head-Mounted Display (HMD), (B)visualizing the contents in an HMD,and (C)visualizing the contents in an HMD where participants can see their hands and take notes. The experiment simulates an immersive communication where participants attend conversations of different genres and from different acquisition perspectives in the context of international experiences. Video quality is evaluated with Single-Stimulus Discrete Quality Evaluation (SSDQE) methodology. Spatial and social presence are evaluated with questionnaires adapted from the literature. Initial empathy is assessed with Interpersonal Reactivity Index(IRI) and a questionnaire is designed to evaluate attitude. Attention is evaluated with 3 questions that had pass/fail answers. 54 participants were evenly distributed among A, B, and C conditions taking into account their international experience backgrounds, obtaining a diverse sample of participants. The results from the subjective test validate the proposed methodology in VR communications, showing that video quality experiments can be adapted to conditions imposed by experiments focused on the evaluation of socioemotional features in terms of contents of long-duration, actor and observer acquisition perspectives, and genre. In addition, the positive results related to the sense of presence imply that technology can be relevant in the analyzed use case. The acquisition perspective greatly influences social presence and all the contents have a positive impact on all participants on their attitude towards international experiences. The annotated dataset, Student Experiences Around the World dataset (SEAW-dataset), obtained from the experiment is made publicly available.

Ester Gonzalez-Sosa, Pablo Perez, Ruben Tolosana et al.

In this study, we focus on the egocentric segmentation of arms to improve self-perception in Augmented Virtuality (AV). The main contributions of this work are: i) a comprehensive survey of segmentation algorithms for AV; ii) an Egocentric Arm Segmentation Dataset, composed of more than 10, 000 images, comprising variations of skin color, and gender, among others. We provide all details required for the automated generation of groundtruth and semi-synthetic images; iii) the use of deep learning for the first time for segmenting arms in AV; iv) to showcase the usefulness of this database, we report results on different real egocentric hand datasets, including GTEA Gaze+, EDSH, EgoHands, Ego Youtube Hands, THU-Read, TEgO, FPAB, and Ego Gesture, which allow for direct comparisons with existing approaches utilizing color or depth. Results confirm the suitability of the EgoArm dataset for this task, achieving improvement up to 40% with respect to the original network, depending on the particular dataset. Results also suggest that, while approaches based on color or depth can work in controlled conditions (lack of occlusion, uniform lighting, only objects of interest in the near range, controlled background, etc.), egocentric segmentation based on deep learning is more robust in real AV applications.

Lara Muñoz, César Díaz, Marta Orduna et al.

To properly evaluate the performance of 360VR-specific encoding and transmission schemes, and particularly of the solutions based on viewport adaptation, it is necessary to consider not only the bandwidth saved, but also the quality of the portion of the scene actually seen by users over time. With this motivation, we propose a robust, yet flexible methodology for accurately assessing the quality within the viewport along the visualization session. This procedure is based on a complete analysis of the geometric relations involved. Moreover, the designed methodology allows for both offline and online usage thanks to the use of different approximations. In this way, our methodology can be used regardless of the approach to properly evaluate the implemented strategy, obtaining a fairer comparison between them.