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.

In this contribution a study dedicated to understanding the influence of the presence of human faces in a 360° image on human perception is presented. Extensive research on saliency estimation in 2D images has shown that the presence of faces attracts human attention. Following these studies, recent 2D image quality assessment methods exploit face detection systems in their models. The application of these concepts to 360° image is not straightforward. Furthermore, existing literature lacks a comparative study between the performance of face detection algorithms on various types of images (2D, fisheye, and omnidirectional) and how detected faces affect the procedure of saliency estimation. In this direction, we analyze the importance of person faces in a scene, by performing a set of subjective tests. From the performed analysis, it results that, even in 360° images, human faces represent an important factor for image saliency. However, giving equal importance to all the detected faces does not lead to a better saliency estimation. Therefore, in this work, a study on the possible use of the face detector in estimating the salience of the 360° image is performed.

Recommender system is a popular tool that aims to provide personalized suggestions to user about items, products, services, and so on. Recommender system has effectively been used in online social networks, especially the location-based social networks for providing suggestions for interesting places known as POIs (points-of-interest). Popular recommender systems explore historical data to learn users’ preferences and, subsequently, they recommend locations to an active user. This strategy faces a major problem when a new POI or business evolves in a city. New business has no historical user experience data. Thus, a recommender system fails to gather enough knowledge about the new businesses, resulting in ignoring them during recommendations. This scenario is popularly known as a cold-start POI problem. Users never get recommendations of the new businesses in a city even though they can be relevant to a user. Also, from a business owner’s perspective, such a recommendation strategy does not help its reachability among users. Therefore, it is important for a recommender system to remain updated with new businesses in a city and ensure that all relevant POIs are recommended to a user irrespective of their lifetime. A POI recommendation approach is proposed in this work that can effectively handle the new businesses, or the cold-start POI problem, in a city. We crowdsource descriptions of cold-start POIs from various online social networks. The reviews of users are exploited here to learn the inherent features at the existing POIs and the new crowdsourced POIs. Finally, the proposed approach recommends top- K POIs consisting of the existing and new POIs. We perform experiments on the real-world Yelp dataset, which is one of the largest available data resources containing details on a wide range of businesses, users, and reviews. The proposed approach is compared with four existing POI recommendation approaches. The obtained results show that our approach outperforms others in handling cold-start POIs.

In this paper we present a technique for saliency estimation in 360 images. Existing approaches exploit high/low-level image features, head movement, and eye-gazes of observers. However, we believe that the saliency of an image is influenced also by its content. The proposed approach consists of three modules. In the first module, a segmentation of the image is performed and its local and global features are extracted. The second module combines the extracted features for summarizing the content that the image portrays. Finally, this content knowledge is utilised for image saliency estimation. Experimental results show effectiveness of the proposed system with respect to ground truth saliency maps.