Publications

There is an increased interest in understanding users' behavior when exploring omnidirectional (360˚) videos, especially in the presence of spatial audio. Several studies demonstrate the effect of no, mono, or spatial audio on visual saliency. However, no studies investigate the influence of higher-order (i.e., 4t h- order) Ambisonics on subjective exploration in virtual reality settings. In this work, a between-subjects test design is employed to collect users' exploration data of 360˚ videos in a free-form viewing scenario using the Varjo XR-3 Head Mounted Display, in the presence of no, mono, and 4th-order Ambisonics audio. Saliency information was captured as head-saliency in terms of the center of a viewport at 50 Hz. For each item, subjects were asked to describe the scene with a short free-verbalization task. Moreover, cybersickness was assessed using the simulator sickness questionnaire at the beginning and at the end of the test. The head-saliency results over time show that with the presence of higher-order Ambisonics audio, subjects concentrate more on the directions sound is coming from. No influence of audio scenario on cybersickness scores was observed. From the analysis of the verbal scene descriptions, it was found that users were attentive to the omnidirectional video, but only for the ‘no audio’ scenario provided minute and insignificant details of the scene objects. The audiovisual saliency dataset is made available following the open science approach already used for the audiovisual scene recordings we previously published. The data is sought to enable training of visual and audiovisual saliency prediction models for interactive experiences.

Audiovisual asynchrony is a significant factor im-pacting the Quality of Experience (QoE), especially for interactive communication like video conferencing. In this paper, we propose a client-side approach to predict the delay between an audio and a video signal, using only the media signals from both streams. Features are extracted from the video and audio stream, respectively, and analyzed using a cross-correlation approach to determine the actual delay. Our approach predicts the delay with an accuracy of over 80% in a time frame of ±1s. We further highlight the potential drawbacks of using a cross-correlation-based analysis and propose different solutions for practical implementations of a delay-based QoE metric.

This paper uses a crowdsourced dataset of online video streaming sessions to investigate opportunities to reduce the power consumption while considering QoE. For this, we base our work on prior studies which model both the end-user's QoE and the end-user device's power consumption with the help of high-level video features such as the bitrate, the frame rate, and the resolution. On top of existing research, which focused on reducing the power consumption at the same QoE optimizing video parameters, we investigate potential power savings by other means such as using a different playback device, a different codec, or a predefined maximum quality level. We find that based on the power consumption of the streaming sessions from the crowdsourcing dataset, devices could save more than 55% of power if all participants adhere to low-power settings.

Instagram and Flickr are just two examples of photo-sharing platforms which are currently used to upload thousands of images on a daily basis. One important aspect in such social media contexts is to know whether an image is of high appeal or not. In particular, to understand the composition of a photo and to improve reading flow, several photo rules have been established. In this paper, we focus on eight selected photo rules. To automatically predict whether an image follows one of these rules or not, we train 13 deep neural networks in a transfer-learning setup and compare their prediction performance. As a dataset, we use photos downloaded from Flickr with specifically selected image tags, which reflect the eight photo rules. There-fore, our dataset does not need additional human annotations. ResNet50 has the best prediction performance, however, there are images that follow several rules, which must be addressed in follow-up work. The code and the data (image URLs) are made publicly available for reproducibility.

This study uses a mixed between- and within-subjects test design to evaluate the influence of interactive formats on the quality of binaurally rendered 360&ring; spatial audio content. Focusing on ecological validity using real-world recordings of 60 s duration, three independent groups of subjects () were exposed to three formats: audio only (A), audio with 2D visuals (A2DV), and audio with head-mounted display (AHMD) visuals. Within each interactive format, two sessions were conducted to evaluate degraded audio conditions: bit-rate and Ambisonics order. Our results show a statistically significant effect (p < .05) of format only on spatial audio quality ratings for Ambisonics order. Exploration data analysis shows that format A yields little variability in exploration, while formats A2DV and AHMD yield broader viewing distribution of 360&ring; content. The results imply audio quality factors can be optimized depending on the interactive format.