Extended reality (XR) technologies are transforming domains like gaming, healthcare, and training, offering benefits such as enhanced safety, cost reduction, and immersive experiences. However, current XR systems often overlook accessibility for individuals with diverse abilities and remain unexplored in inclusive learning contexts. The Incluverso-5G project seeks to address this gap by leveraging XR to improve the lives of individuals with intellectual disabilities, including employment inclusion. This paper presents an immersive tool to support remote training for certified cooking courses, enabling users unable to attend in-person classes to access training and certification.The system includes an advanced 360-degree content viewer and a real-time immersive supervision tool, utilizing features like six degrees of freedom (6-DoF), color passthrough, and hand tracking for interactive learning. A four-step remote learning process—sensitization, instructional content, supervised practice, and certification—ensures accessibility and usability. The tool empowers individuals to acquire valuable skills and achieve professional certifications, demonstrating the potential of XR for inclusive training.

Cognitive training is a common therapeutic approach to improve the quality of life of individuals with intellectual disabilities, although conventional methods often show limited engagement and skill transfer. The use of eXtended Reality (XR) allows to develop immersive cognitive training, with evidence of improved motivation and engagement. In parallel, biosignals and behavioral metrics are increasingly used to assess user experience and cognitive state in immersive environments. This study presents a longitudinal evaluation of an immersive cognitive training application involving eight participants over eight months. Multiple data sources were collected, including eye tracking, electromyography and electrodermal activity, together with questionnaires and interviews to capture subjective experience. Preliminary results focus on gaze and interaction data. The number of fixations reflects task complexity, with more complex scenarios associated with increased exploratory behavior and a higher proportion of fixations on distracting elements. Interaction data from the most complex task indicate improved efficiency over time both before and during actions. Further analysis will explore these findings and incorporate the remaining physiological and behavioral data, together with the analysis of the interviews and questionnaires.

Assessing the quality of 3D reconstructed models remains a key challenge in multimedia applications, especially in the context of cultural heritage, where visual fidelity and perceptual realism are equally crucial. This study investigates how reconstruction parameters, as well as existing objective quality metrics, align with human perception. In addition, we analyze how perceived quality and user interaction are related. A dataset of 3D models was generated by varying the number of input images, mesh complexity, and texture resolution. Results from a subjective study show that texture resolution significantly affects perceived quality, whereas variations in number of images and mesh complexity have a limited impact. Furthermore, interaction behavior was found to vary with perceived quality, with participants spending more time and exploring larger viewing angles for models receiving higher scores. These findings highlight the need for perceptually grounded, interaction-aware evaluation methodologies and provide guidelines for future perceptual optimization of 3D reconstruction pipelines.

Extended reality technologies are increasingly used in cultural heritage for preserving and accessing sites and artworks, where 3D model acquisition and rendering are key. Despite progress in reconstruction methodologies, a standardized approach to quality assessment is still missing. This study aims to evaluate objective quality metrics —both image-based and model-based, Full Reference and No Reference— applied to 3D models generated using the Structure from Motion algorithm. By varying parameters such as the number of images, number of triangles, and texture resolution, we examine the impact of these factors on metric outcomes, aiming to assess their reliability in cultural heritage applications.

In this demo, we showcase a system that applies extended reality (XR) and volumetric video to enhance cultural experiences. The platform enables real-time interaction between users and a presenter within a virtual environment enriched with 3D assets. Users can visualize a volumetric representation of the presenter captured in real-time using a Free Viewpoint Video (FVV) system. Additionally, the presenter can control the elements in the scene using hand gestures, recognized by an artificial intelligence (AI) model. We propose a methodology to assess the subjective quality of this experience through user studies. The complete system will be demonstrated, including the real-time volumetric capture and the immersive application to participate in the cultural experience.

Figure 1: Three different hand representations based on different pass through techniques while users are performing three different tasks.

The increasing deployment of immersive technologies is opening up opportunities in the field of behavioral therapies.In particular, the use of eXtended Reality technologies allows the development of therapies that require bringing users to a remote location without

The combination of eXtended Reality (XR) technologies with physiological signals offers innovative approaches to cognitive training for individuals with intellectual disabilities.This study presents a tool designed for cognitive training aimed at job integration through interactive and immersive environments.The tool was developed in collaboration with therapists from the Juan XXIII Foundation, an occupational center, and focuses on enhancing cognitive skills through serious games for job training.These games simulate realistic environments, such as a cafeteria and a supermarket, where users must complete various tasks.These tasks are designed to progressively increase in complexity as users advance, allowing them to improve their skills.Therapists can monitor and control the training sessions in real-time through an application, while users engage with the immersive scenarios.Additionally, the system incorporates non-invasive biosensors to track physiological data, such as heart rate, eye movement, etc. providing valuable insights into the

Abstract Quality assessment is a key element for the evaluation of hardware and software involved in image and video acquisition, processing, and visualization. In the medical field, user-based quality assessment is still considered more reliable than objective methods, which allow the implementation of automated and more efficient solutions. Regardless of increasing research on this topic in the last decade, defining quality standards for medical content remains a non-trivial task, as the focus should be on the diagnostic value assessed by expert viewers rather than the perceived quality from naïve viewers, and objective quality metrics should aim at estimating the first rather than the latter. In this paper, we present a survey of methodologies used for the objective quality assessment of medical images and videos, dividing them into visual quality-based and task-based approaches. Visual quality-based methods compute a quality index directly from visual attributes, while task-based methods, being increasingly explored, measure the impact of quality impairments on the performance of a specific task. A discussion on the limitations of state-of-the-art research on this topic is also provided, along with future challenges to be addressed.

In this work, we propose a demo of an immersive videoconference system using Free Viewpoint Video (FVV) technology. It makes use of the FVV Live system, which covers the entire FVV pipeline (capture, view rendering, and visualization) while working in real-time. The FVV Live system consists of nine cameras that capture an environment and a view renderer that uses the information from the cameras to generate a synthetic view at an arbitrary point.It is designed as a hybrid demo. While the capture and rendering processes take place at our premises, FVV Live can be visualized through devices connected to the Internet.The system allows immersive navigation of a virtual scene with 6 degrees of freedom, and interaction with live-captured avatars integrated in such scene. For this purpose, it uses WebRTC connections to update the position of the virtual camera and to receive the FVV Live view encoded as a video.Additionally, the user will be recorded by a simple camera and microphone setup, and the generated streams will be transmitted to our premises through the same WebRTC server. This way, people being recorded by FVV Live will be able to see and hear the user, enabling bidirectional communication.

Eye tracking has historically been a very popular tool. The data it records allow us to understand how people behave and what they attend to within our visual world; under this perspective the experiments, applications and use-cases are endless. Therefore, it is not surprising to witness a strong rise in the use of eXtended Reality (XR) devices with embedded eye trackers in research. These devices allow for less obtrusive experimenting conditions, and a significantly higher experimental control compared to traditional desktop testing. The use of eye tracking in XR is increasing and so is the need for a toolbox enabling consensus about eye tracking methods in 3D. We present the Salient360! toolbox: it implements functions to identify saccades and fixations and output gaze features (e.g., saccade directions) to generate saliency maps, fixation maps, and scanpath data. It implements comparisons of gaze data with methods adapted to 3D. We plan continuous improvements of the toolbox as the community develops new tools and methods dedicated to 360°gaze tracking. We hope that this toolbox will spark discussions about the methodology of 3D gaze processing, facilitate running experiments, and improve studying gaze in 3D. https://github.com/David-Ef/salient360Toolbox

Immersive technologies like eXtended Reality (XR) are the next step in videoconferencing. In this context, understanding the effect of delay on communication is crucial. This article presents the first study on the impact of delay on collaborative tasks using a realistic Social XR system. Specifically, we design an experiment and evaluate the impact of end-to-end delays of 300, 600, 900, 1,200, and 1,500 ms on the execution of a standardized task involving the collaboration of two remote users that meet in a virtual space and construct block-based shapes. To measure the impact of the delay in this communication scenario, objective and subjective data were collected. As objective data, we measured the time required to execute the tasks and computed conversational characteristics by analyzing the recorded audio signals. As subjective data, a questionnaire was prepared and completed by every user to evaluate different factors such as overall quality, perception of delay, annoyance using the system, level of presence, cybersickness, and other subjective factors associated with social interaction. The results show a clear influence of the delay on the perceived quality and a significant negative effect as the delay increases. Specifically, the results indicate that the acceptable threshold for end-to-end delay should not exceed 900 ms. This article additionally provides guidelines for developing standardized XR tasks for assessing interaction in Social XR environments.

Eye tracking can serve as a gateway to studying the mind. For this reason it has been adopted by a diverse range of scientific communities. With the improvement of the quality of head-mounted virtual reality devices (HMDs) over the past 10 years, eye tracking has been added to capture gaze in immersive environments. The use of HMDs with eye tracking is increasing significantly and so is the need for a toolbox enabling consensus about eye tracking methods in 3D. We present the Salient360! toolbox: it implements functions to identify saccades and fixations and output gaze characteristics (e.g., fixation duration or saccade directions), to generate saliency maps, fixation maps, and scanpath data. It also implements routines made to compare gaze data that were adapted to 3D. We hope that this toolbox will spark discussions about the methodology of 3D gaze processing, facilitate running experiments, and improve the gaze study in 3D. https://github.com/David-Ef/salient360Toolbox

This paper presents a comparison between three content-immersive methodologies to evaluate video quality: Absolute Category Rating (ACR) with unrepeated scenes, Single Stimulus Continuous Quality Evaluation (SSCQE), and Single Stimulus Discrete Quality Evaluation (SSDQE). Three tests were conducted with different participants assigned to each methodology to assess 360-degree video quality in an immersive communications context. Additionally, socioemotional aspects such as presence, attitude, or attention were measured for each condition. All three methodologies offer similar results in terms of video quality evaluation, and they all keep similar levels of social and spatial presence. Both SSCQE and SSDQE allow narrative and context while evaluating video quality, but SSDQE has less impact in participants' attention to the video content. These results suggest that it is possible to extend ITU-T P.919 with the support of long sequences with in-sequence SSDQE video quality evaluation, without significant impact on the socioemotional properties of the experience. This would result on a standard content-immersive method, methodology suitable for 360-degree video content, favoring the ecological validity of the subjective assessment tests.

This paper proposes a methodology to assess video quality, spatial and social presence, empathy, attitude, and attention in 360-degree videos for immersive communications. The methodology is validated in an experiment which simulates an immersive communication environment where participants attend three conversations of different genre (everyday conversation, educational, and discussion) and from actor and observer acquisition perspectives. We consider three experimental conditions: (A) visualizing and rating the perceptual 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. In all conditions participants visualize the same 360-degree videos, designed and acquired in the context of international experiences. Fifty-four participants were evenly distributed among A, B, and C conditions taking into account their international experience backgrounds (working or studying in a foreign country), obtaining a balanced and diverse sample of participants. Finally, the annotated dataset, Student Experiences Around the World dataset (SEAW-dataset), obtained from the experiment is made publicly available for the research community.

The rise of eXtended Reality (XR) has led to multiple ways of including the user’s body in interactive experiences. However, the delay limits of self-view rendering in interactive XR remain unexplored. This article presents a minimum self-view latency system and an interactive task-based experiment to study the influence of different levels of self-view delay on Quality of Experience (QoE) and task performance. During the experiment, 23 users tested 8 delay conditions (from 190 to 597 ms) while building block-based models. The results show a hard threshold in terms of involvement and overall quality around 450ms. However, the impact on adaptation and execution time was less pronounced. This indicates that although users adapted to the task in a certain way, their immersion was severely affected above a certain self-view delay value.

Tele-education was already a solution for people who cannot attend lessons in person (such as inaccessibility in rural areas or illness issues). However, COVID has revealed problems in tele-education with current technology, causing adolescents and children to slow down their learning curves and experience problems of social distancing with their classmates. This paper presents a user study to validate an immersive communication system for tele-education purposes. This system streams in real time a class using 360-degree cameras, allowing remote students to explore the whole scene and improving the feeling of being in the classroom with their colleagues. Additionally, the prototype provides notifications to the remote students about events (such as a changes in the teacher’s presentation or classmates raising their hands) that occur outside their viewport to indicate in which direction they should move their heads to visualize them.

Recently an impressive development in immersive technologies, such as Augmented Reality (AR), Virtual Reality (VR) and 360${^\circ }$video, has been witnessed. However, methods for quality assessment have not been keeping up. This paper studies quality assessment of 360${^\circ }$video from the cross-lab tests (involving ten laboratories and more than 300 participants) carried out by the Immersive Media Group (IMG) of the Video Quality Experts Group (VQEG). These tests were addressed to assess and validate subjective evaluation methodologies for 360${^\circ }$video. Audiovisual quality, simulator sickness symptoms, and exploration behavior were evaluated with short (from 10 seconds to 30 seconds) 360${^\circ }$sequences. The following factors’ influences were also analyzed: assessment methodology, sequence duration, Head-Mounted Display (HMD) device, uniform and non-uniform coding degradations, and simulator sickness assessment methods. The obtained results have demonstrated the validity of Absolute Category Rating (ACR) and Degradation Category Rating (DCR) for subjective tests with 360${^\circ }$videos, the possibility of using 10-second videos (with or without audio) when addressing quality evaluation of coding artifacts, as well as any commercial HMD (satisfying minimum requirements). Also, more efficient methods than the long Simulator Sickness Questionnaire (SSQ) have been proposed to evaluate related symptoms with 360${^\circ }$videos. These results have been instrumental for the development of the ITU-T Recommendation P.919. Finally, the annotated dataset from the tests is made publicly available for the research community.

This work-in-progress presents a dataset of 360degree videos, called EVENT-CLASS, with associated characteristics in the context of tele-education. The sequences (video and audio) have been captured considering several environments, lighting conditions, acquisition perspectives, and cameras, enriching the dataset. EVENT-CLASS will be helpful for numerous applications related to tele-education, including quality assessment tests, and with the aim of improving the immersive experience of remote users thanks to the detection of relevant events that happen in the class. In this sense, this paper presents preliminary results of using transfer learning for person detection in 360degree scenes, based on Detectron2, and provides insights on the influence of applying it to equirectangular projection and over the viewport. Ongoing works will allow to include more videos and ground-truth annotations to the dataset.

Current extended reality applications use omnidirectional video to boost users' immersion and sense of presence. As contents from distant video sources cannot be instantaneously delivered, the end-to-end delay becomes a key problem when user actions cannot be simultaneously matched by system reactions. Thus, we have designed and executed an experiment to assess its influence on the quality of experience, the sense of presence, and the sickness caused. To do it, we have developed a viewport adaptive simulator to render simultaneously two layers of immersive video to allow different adaptation schemes and delay values. Twenty observers have assessed 180 test videos from 9 sources. Our analysis shows a clear influence of the delay condition and the adaptation scheme on the perceived quality. Moreover, it also shows that the adaptation schemes and delay conditions have a small influence on the sense of presence and little effect on observers sickness.

This paper presents a system and methodology for the analysis of quality of experience factors for dense multiview (MV) video using a head-mounted device (HMD). An MV-HMD player has been designed and implemented to immerse the users in a virtual environment, where they are placed in front of a virtual lightfield display that shows a different viewpoint depending on the position of their head. This paper describes a methodology for the analysis of the subjective perception of the transition among views (motion parallax), which is specific to the visualization of MV content. While previous works simulated the user movement by predefined view paths or used complex devices to track them, this system allows the observer to move freely, varying the perspective of the scene while easily tracking the observer's position. This paper is, up to our knowledge, the first providing a complete framework for the assessment of this subjective factor using an HMD. The subjective results obtained using this framework are used to 1) assess the influence of the user movement, display settings, and content characteristics in the perception of smoothness in the view transition, and 2) analyze the performance and limitations of a prediction model for subjective smoothness scores.

Social difficulties are the hallmark features of Autism Spectrum Disorder (ASD) and can lead to atypical visual attention towards stimuli. Eye movements encode rich information about attention and psychological factors of an individual, which could help to characterize the traits of ASD. Learning atypical eye movements of the individuals with ASD towards various stimuli is important and has many application scenarios. However, due to the lack of open datasets, research in this sense is still limited. In this work, we present an open dataset of eye movements of children with Autism Spectrum Disorder. It consists of 300 natural scene images and the corresponding eye movement data collected from 14 children with ASD and 14 healthy controls. In particular, fixation maps and scanpaths are available in the dataset. Based on this dataset, researchers could analyze the visual traits of children with ASD and design specialized visual attention models to promote research in related fields, as well as design specialized models to identify the individuals with ASD. The dataset can be accessed in http://doi.org/10.5281/zenodo.2647418

Research on visual attention in 360° content is crucial to understand how people perceive and interact with this immersive type of content and to develop efficient techniques for processing, encoding, delivering and rendering. And also to offer a high quality of experience to end users. The availability of public datasets is essential to support and facilitate research activities of the community. Recently, some studies have been presented analyzing exploration behaviors of people watching 360° videos, and a few datasets have been published. However, the majority of these works only consider head movements as proxy for gaze data, despite the importance of eye movements in the exploration of omnidirectional content. Thus, this paper presents a novel dataset of 360° videos with associated eye and head movement data, which is a follow-up to our previous dataset for still images [14]. Head and eye tracking data was obtained from 57 participants during a free-viewing experiment with 19 videos. In addition, guidelines on how to obtain saliency maps and scanpaths from raw data are provided. Also, some statistics related to exploration behaviors are presented, such as the impact of the longitudinal starting position when watching omnidirectional videos was investigated in this test. This dataset and its associated code are made publicly available to support research on visual attention for 360° content.

Virtual Reality (VR) provides the users with new immersive media experiences, offering the possibility to freely explore 360° content. Understanding these new exploration behaviors is crucial for the development of efficient techniques for processing, coding, delivering and rendering omnidirectional content to offer the highest possible Quality of Experience (QoE). Progress has already been made on visual attention (VA) modeling for 360° content. In this paper we briefly review the current status of research on this topic that led us to propose a benchmarking platform for evaluating and comparing the performance of models for saliency and scanpath prediction for 360° content. This paper introduces the `UN Salient360! benchmark” platform featuring a dataset, a toolbox and a framework for evaluation of different class of models. This online platform can be found in httns://salient360.ls2n.fr/.

Understanding how observers watch visual stimuli like Images and Videos has helped the multimedia encoding, transmission, quality assessment and rendering communities immensely, to learn the regions important to an observer and provide to him/her an optimum quality of experience. The problem is even more paramount in case of 360 degree stimuli considering that most/a part of the content might not be seen by the observers at all, while other regions maybe extraordinarily important. Attention studies in this area has however been missing, mainly due to the lack of a dataset and guidelines to evaluate and compare visual attention/saliency in such scenarios. In this work, we present a dataset of sixty different 360 degree images, each watched by at-least 40 observers. Additionally, we also provide guidelines and tools to the community regarding the procedure to evaluate and compare saliency in omni-directional images. Some basic image/ observer agnostic viewing characteristics, like variation of exploration strategies with time and expertise, and also the effect of eye-movement within the view-port are explored. The dataset and tools are made available for free use by the community and is expected to promote Reproducible Research for all future work on computational modeling of attention in 360 scenarios.

Light field technology may have a positive impact on several multimedia applications thanks to novel ways to explore the captured scenes, such as changing the parallax (horizontally and vertically) and refocusing the content. These innovative use cases require new considerations that affect the whole processing chain, from content acquisition to visualization, as well as the methodologies for quality evaluation. In particular, capturing and selecting the appropriate content is crucial for a successful development and evaluation of audiovisual technologies. Thus, this paper presents a framework addressing the reconsideration of the space of attributes for an adequate characterization of light field data. Firstly, an exhaustive characterization of light field content is described, based on various particular features, including depth and refocusing properties to traditional spatial, temporal, and color indicators. Then, based on this characterization, specific techniques are proposed for an effective selection of light field content for perceptual quality assessment.

The subjective assessment of super multiview (SMV) video considers two main perceptual factors: image quality and visual comfort at the viewpoint transition. While previous works only covered raw content with high levels of visual comfort, this work supersedes them by targeting the subjective assessment of SMV content with coding artifacts. The outcome of this analysis yields important conclusions regarding the relationship between these two factors, indicating that the perceived image quality is independent from the view point change speed, and the perceived visual comfort at the view point transition is independent from the image quality. These conclusions facilitate the extension of the scope of existing subjective perception models, designed for raw SMV content, to coded content.

Nowadays, HTTP adaptive streaming (HAS) has become a reliable distribution technology offering significant advantages in terms of both user perceived Quality of Experience (QoE) and resource utilization for content and network service providers. By trading-off the video quality, HAS is able to adapt to the available bandwidth and display requirements so that it can deliver the video content to a variety of devices over the Internet. However, until now there is not enough knowledge of how the adaptation techniques affect the end user's visual experience. Therefore, this paper presents a comparative analysis of different bitrate adaptation strategies in adaptive streaming of monoscopic and stereoscopic video. This has been done through a subjective experiment of testing the end-user response to the video quality variations, considering the visual comfort issue. The experimental outcomes have made a good insight into the factors that can influence on the QoE of different adaptation strategies.

Although the delivery of 3D video services to households is nowadays a reality thanks to frame-compatible formats, many efforts are being made to obtain efficient methods to transmit 3D content offering a high quality of experience to the end users. In this paper, a stereoscopic video streaming scenario is considered, and the perceptual impact of various strategies applicable to adaptive streaming situations are compared. Specifically, the mechanisms are based on switching between copies of the content with different coding qualities, on discarding frames of the sequence, on switching from 3D to 2D, and on using asymmetric coding of the stereo views. In addition, when video freezes happen, the possibility of keeping the end-to-end latency or maintaining the continuity of the video are considered. These aspects were evaluated carrying out a subjective assessment test, considering also visual discomfort issues, using a methodology designed to keep as far as possible domestic viewing conditions.

Real-time monitoring of multimedia Quality of Experience is a critical task for the providers of multimedia delivery services: from television broadcasters to IP content delivery networks or IPTV. For such scenarios, meaningful metrics are required which can generate useful information to the service providers that overcome the limitations of pure Quality of Service monitoring probes. However, most of objective multimedia quality estimators, aimed at modeling the Mean Opinion Score, are difficult to apply to massive quality monitoring. Thus we propose a lightweight and scalable monitoring architecture called Qualitative Experience Monitoring (QuEM), based on detecting identifiable impairment events such as the ones reported by the customers of those services. We also carried out a subjective assessment test to validate the approach and calibrate the metrics. Preliminary results of this test set support our approach.

The increase of multimedia services delivered over packet-based networks has entailed greater quality expectations of the end-users. This has led to an intensive research on techniques for evaluating the quality of experience perceived by the viewers of audiovisual content, considering the different degradations that it could suffer along the broadcasting system. In this paper, a comprehensive study of the impact of transmission errors affecting video and audio in IPTV is presented. With this aim, subjective assessment tests were carried out proposing a novel methodology trying to keep as close as possible home environment viewing conditions. Also 3DTV content in side-by-side format has been used in the experiments to compare the impact of the degradations. The results provide a better understanding of the effects of transmission errors, and show that the QoE related to the first approach of 3DTV is acceptable, but the visual discomfort that it causes should be reduced.