Publications

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.

The research on the new generation mobile networks is currently in the phase of defining the key technologies to make 6G successful. Hereby, the research project 6G NeXt is aiming to provide a tight integration between the communication network, consisting of the radio access as well as backbone network, and processing facilities. By the concept of split computing, the processing facilities are distributed over the entire backbone network, from centralised cloud to the edge cloud at a base station. Based on two demanding use cases, Smart Drones and Hologradic Communication, we investigate a joint communication and compute architecture that will make the application of tomorrow become reality.