Abstract
With the appearance of innovative virtual reality (VR) technologies, the need to create immersive content arose. Although there are already some non-immersive solutions to address immersive audio-visual content, there are no solutions that allow the creation of immersive multisensory content. This work proposes a novel architecture for a collaborative immersive tool that allows the creation of multisensory VR experiences in real-time, thus promoting the expeditious development, adoption, and use of immersive systems and enabling the building of custom-solutions that can be used in an intuitive manner to support organizations' business initiatives. To validate the presented proposal, two approaches for the authoring tools (Desktop interface and Immersive interface) were subjected to a set of tests and evaluations consisting of a usability study that demonstrated not only the participants' acceptance of the authoring tool but also the importance of using immersive interfaces for the creation of such VR experiences.

Abstract
Virtual reality (VR) games have the potential to produce immersive experiences. To better explore the potential of VR games, it becomes necessary to understand what affects the player's presence in VR games. This work measures and compares the levels of presence and cybersickness in VR environments. Two games with different levels of interaction and narrative were compared. Presence and cybersickness were measured in a sample of 32 subjects using the IPQp questionnaire and a Portuguese version of the SSQ respectively. The results indicate that there were no differences in presence and cybersickness between the interaction and the narrative dimensions. To extend the study, the gender of participants was also considered an independent variable where we found significant differences in the metrics of presence and experienced realism, nausea and disorientation with female participants getting higher scores. © 2018 IEEE.

Abstract
Virtual Reality (VR) has as a key feature, the users’ interaction with a virtual environment. Depending on the purpose of a given VR application, it can be essential to use multisensory stimulus without biasing users towards specific actions or decisions in the virtual environment (VE). The goal of the present work is to study if the choice of paths can be influenced by the addition of multisensory stimulus when navigating in a VE using an immersive setup. The awareness of having to take such decisions was also considered. For the purpose, we used a VR game-like application contemplating three levels. Each level was symmetrical and had two possible paths to move to the next level (left or right). For each level, there was a multisensory stimulus on the right path (from a subject orientation): wind, vibration, scent respectively. The sample of the study consisted of 50 participants, and the results showed that none of the multisensory stimuli had a significant impact users’ decision. The users’ awareness of having to decide also did not affect their path. We conclude that multisensory stimuli can be used to raise the credibility of the virtual environments without compromising the users’ decisions. © Springer Nature Switzerland AG 2019.

Abstract
Recently, there has been a significant progress in the research and development of the high dynamic range (HDR) video technology and the state-of-the-art video pipelines are able to offer a higher bit depth support to capture, store, encode, and display HDR video content. In this paper, we introduce a novel HDR video compression algorithm, which uses a perceptually uniform color opponent space, a novel perceptual transfer function to encode the dynamic range of the scene, and a novel error minimization scheme for accurate chroma reproduction. The proposed algorithm was objectively and subjectively evaluated against four state-of-the-art algorithms. The objective evaluation was conducted across a set of 39 HDR video sequences, using the latest x265 10-bit video codec along with several perceptual and structural quality assessment metrics at 11 different quality levels. Furthermore, a rating-based subjective evaluation (n = 40) was conducted with six sequences at two different output bitrates. Results suggest that the proposed algorithm exhibits the lowest coding error amongst the five algorithms evaluated. Additionally, the rate-distortion characteristics suggest that the proposed algorithm outperforms the existing state-of-the-art at bitrates >= 0.4 bits/pixel.

Abstract
Consumer High Dynamic Range (HDR) displays are appearing on the market. Capable of generating a peak luminance of up to 2,000 nits, the improved dynamic range they provide can only be perceived when viewed in a dark environment. In this paper, we present a display architecture that is capable of generating a peak luminance of 10,000 nits. We demonstrate, with a subjective evaluation, that the increased peak luminance is required to perceive a high dynamic range in bright ambient environments. Furthermore, we show that by fitting a surface through the data, we can predict the dynamic range that can be perceived from the luminance and illuminance with low error. We can also invert the prediction to estimate the required peak luminance for a particular combination of dynamic range and ambient lighting.

Abstract
High dynamic range (HDR) displays are capable of displaying a wider dynamic range of values than conventional displays. As HDR content becomes more ubiquitous, the use of these displays is likely to accelerate. As HDR displays can present a wider range of values, traditional strategies for mapping HDR content to low dynamic range (LDR) displays can be replaced with either directly displaying values, or using a simple shift mapping (exposure adjustment). The latter approach is especially important when considering ambient lighting, as content viewed in a dark environment may appear substantially different to a bright one. This paper seeks to identify an exposure value which is suitable for displaying specific HDR content on an HDR display under a range of ambient lighting levels. Based on data captured with human participants, this paper establishes user preferred exposure values for a variety of maximum display brightnesses, content and ambient lighting levels. These are then used to develop two models to predict preferred exposure. The first is based on linear regression using straightforward image statistics which require minimal computation and memory to be computed, making this method suitable to be directly used in display hardware. The second is a model based on convolutional neural networks (CNN) to learn image features which best predict exposure values. The CNN model generates better results than the first model at the cost of memory and computation time.