This paper presents a novel multimodal dataset for the analysis of Quality of Experience (QoE) in emerging immersive multimedia applications. In particular, the perceived Sense of Presence (SoP) induced by one-minute long video stimuli is explored with respect to content, quality, resolution, and sound reproduction and annotated with subjective scores. Furthermore, a complementary analysis of the recorded physiological signals, such as EEG, ECG, and respiration is carried out, aiming at an alternative evaluation of human experience while consuming immersive multimedia content. Results confirm the value of the introduced dataset and its consistency for the purposes of QoE assessment for immersive multimedia. More specifically, subjective ratings demonstrate that the created dataset enables distinction between low and high levels of immersiveness, which is also confirmed by a preliminary analysis of recorded physiological signals.
Tremendous progress has been made in audiovisual technologies in the last decades. Consequently, new technologies quality measures evolve and trend to be more user-centric. This is the reason why the Quality of Experience (QoE) assessment is presently meaningful and challenging, especially for users typical experiences during multimedia content consumption. Such an evaluation is the aim of this paper. More specifically, the Sense of Presence (SoP) was explored in place of QoE as it is a factor influencing the QoE. This paper presents the conducted subjective test investigating typical and practical user experiences. This latter consists of presenting one-minute video stimuli to twenty subjects, on three different devices (iPhone, iPad and UHD screen). Annotated subjective scores were collected and physiological signals (EEG, ECG, and Respiration) were recorded during the conducted subjective test. The resulting multimodal dataset, aiming an alternative evaluation of human experience while consuming multimedia, is publicly available.
It is exciting to witness the fast development of Unmanned Aerial Vehicle (UAV) imaging which opens the door to many new applications. In view of developing rich and efficient services, we wondered which strategy should be adopted to predict salience in UAV videos. To that end, we introduce here a benchmark of off-the-shelf state-of-theart models for saliency prediction. This benchmark studies comprehensively two challenging aspects related to salience, namely the peculiar characteristics of UAV contents and the temporal dimension of videos. This paper enables to identify the strengths and weaknesses of current static, dynamic, supervised and unsupervised models for drone videos. Eventually, we highlight several strategies for the development of visual attention in UAV videos.
The fast and tremendous evolution of the unmanned aerial vehicle (UAV) imagery gives place to the multiplication of applications in various fields such as military and civilian surveillance, delivery services, and wildlife monitoring. Combining UAV imagery with study of dynamic salience further extends the number of future applications. Indeed, considerations of visual attention open the door to new avenues in a number of scientific fields such as compression, retargeting, and decision-making tools. To conduct saliency studies, we identified the need for new large-scale eye-tracking datasets for visual salience in UAV content. Therefore, we address this need by introducing the dataset EyeTrackUAV2. It consists of the collection of precise binocular gaze information (1000 Hz) over 43 videos (RGB, 30 fps, 1280 × 720 or 720 × 480). Thirty participants observed stimuli under both free viewing and task conditions. Fixations and saccades were then computed with the dispersion-threshold identification (I-DT) algorithm, while gaze density maps were calculated by filtering eye positions with a Gaussian kernel. An analysis of collected gaze positions provides recommendations for visual salience ground-truth generation. It also sheds light upon variations of saliency biases in UAV videos when opposed to conventional content, especially regarding the center bias.
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