Recently an impressive development in immersive technologies, such as Augmented Reality (AR), Virtual Reality (VR) and 360 video, has been witnessed. However, methods for quality assessment have not been keeping up. This paper studies quality assessment of 360 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 video. Audiovisual quality, simulator sickness symptoms, and exploration behavior were evaluated with short (from 10 seconds to 30 seconds) 360 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 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 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.

5G is a key tool for the cloud-based wireless production of audio-visual contents. By providing higher throughput and lower latency than previous mobile network technologies, as well as flexible allocation of network resources, it enables that some content production pipelines, which are currently implemented using wired connectivity, are provided on top of a 5G network. This brings down production costs, reduces environmental impact and increases operational efficiency. In particular, live immersive production services, such as free-viewpoint video (FVV), can be provided in a much more efficient way. In this paper, we address the challenge of producing and streaming FVV services in real time through 5G networks. We have adapted a state-of-the-art FVV system to integrate it within a 5G architecture, using three key technological enablers: mmWave radio access network to support the uplink traffic requirements from FVV cameras, multi-access edge computing to run video processing algorithms with minimum latency, and end-to-end slicing to guarantee a sufficient quality of service (QoS) within the production pipeline. We have built a field trial over the production network of a telecommunication operator, including a mmWave pilot deployment, edge cloud processing, and remote content production, involving three different locations across Spain. We have measured the key performance indicators at the relevant parts of our trial deployment, showing that, with existing 5G technology, it is possible to achieve live FVV production, although with some limitations. We have also analyzed such limitations, obtaining some insights on how the next generation of 5G networks can overcome them to achieve higher quality of experience (QoE).

FVV Live is a novel end-to-end free-viewpoint video system, designed for real-time operation, using consumer-grade cameras and hardware, which enables low deployment costs and easy installation for immersive event-broadcasting or videoconferencing. All the blocks of the system have been designed to maximize perceptual video quality, overcoming the limitations imposed by hardware and network, which impact directly the accuracy of depth data and thus the quality of virtual view synthesis. Therefore, it does not sacrifice perceptual video quality with respect to high-end counterparts. The results presented in this paper correspond to an implementation with nine stereo-based depth cameras. However, the design of the acquisition block of FVV Live allows scalability for an arbitrary number of cameras. In addition, FVV Live presents low motion-to-photon and end-to-end delays, which enables a responsive free-viewpoint navigation and bilateral immersive communications. Moreover, the visual quality of FVV Live has been assessed through subjective assessment with satisfactory results, and additional comparative tests show that it is preferred over state-of-the-art DIBR alternatives.

Vehicle monitoring using camera networks is an important task for traffic applications. Moreover, it becomes critical in nighttime, when the probability of an accident considerably increases as visibility conditions worsen. Typical approaches are mostly based on the assumption that regions delimiting vehicle lights are well defined, so that they are segmented and then associated to vehicle entities. However, this assumption fails in images acquired by existing traffic camera networks, where vehicle lights are revealed as flashes and other complex light patterns, occupying large and even disconnected image regions. In this work, a real-time vehicle detection algorithm for nighttime situations has been presented, which is able to locate vehicles in the image by analyzing the previous complex light patterns. For this purpose, a novel machine learning framework based on a grid of foveal classifiers has been designed. Every classifier in the grid processes the same global image descriptor (only one descriptor is computed per image). However, every one of them is trained to predict a different output depending on the classifier position in the grid and the vehicle ground-truth location. Additionally, only point-based annotations are required to train the grid of foveal classifiers, speeding up the cost of creating the required databases. Experimental results prove the effectiveness of the proposed method in a new created nighttime database with point-based annotations.

A video-aware unequal loss protection (ULP) system for protecting RTP video streaming in bursty packet loss networks is proposed. Considering the relevance of the frame, the state of the channel, and the bitrate constraints of the protection bitstream, our algorithm selects in real time the most suitable frames to be protected through forward error protection (FEC) techniques. It benefits from a wise RTP encapsulation that allows working at a frame level without requiring any further process than that of parsing RTP headers. This makes our system straightforward and fast, perfectly suitable to be included in commercial video streaming servers. Simulation results show how our technique outperforms other proposed ULP schemes.