Publikationen

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.

Video quality assessment is generally important to assess any kind of immersive media technology, for example, to evaluate a captured content, algorithms for encoding and projection, and systems, as well as for technology optimization. This chapter provides an overview of the two types of video quality assessment, subjective testing with human viewers, and quality prediction or estimation using video quality metrics or models. First, viewing tests with humans as the gold standard for video quality are reviewed in light of their instantiation for omnidirectional video (ODV). In the second part of the chapter, the less time-consuming, better scalable second type of assessment with objective video quality metrics and models is discussed, considering the specific requirements of ODV. Often they incorporate computational models of human perception and content properties. ODV introduces the challenges of interactivity compared to standard 2D video and typically spherical projection distortions due to its omnidirectional, “point-of-view” (in terms of camera-shot type) nature. Accordingly, subjective tests for ODV include specific considerations of the omnidirectional nature of the presented content and dedicated head-rotation or even additional eyetracking data capture. In the last part of the chapter, it is shown how to improve objective video quality prediction by taking into account user behavior and projection distortions.

This paper presents a proof-of-concept study conducted to analyze the effect of simple diotic vs. spatial, position-dynamic binaural synthesis on social presence in VR, in comparison with face-to-face communication in the real world, for a sample two-party scenario. A conversational task with shared visual reference was realized. The collected data includes questionnaires for direct assessment, tracking data, and audio and video recordings of the individual participants’ sessions for indirect evaluation. While tendencies for improvements with binaural over diotic presentation can be observed, no significant difference in social presence was found for the considered scenario. The gestural analysis revealed that participants used the same amount and type of gestures in face-to-face as in VR, highlighting the importance of non-verbal behavior in communication. As part of the research, an end-to-end framework for conducting communication studies and analysis has been developed.

There are more and more photographic images uploaded to social media platforms such as Instagram, Flickr, or Facebook on a daily basis. At the same time, attention and consumption for such images is high, with image views and liking as one of the success factors for users and driving forces for social media algorithms. Here, “liking” can be assumed to be driven by image appeal and further factors such as who is posting the images and what they may show and reveal about the posting person. It is therefore of high research interest to evaluate the appeal of such images in the context of social media platforms. Such an appeal evaluation may help to improve image quality or could be used as an additional filter criterion to select good images. To analyze image appeal, various datasets have been established over the past years. However, not all datasets contain high-resolution images, are up to date, or include additional data, such as meta-data or social-media-type data such as likes and views. We created our own dataset “AVT-ImageAppeal-Dataset”, which includes images from different photo-sharing platforms. The dataset also includes a subset of other state-of-the-art datasets and is extended by social-media-type data, meta-data, and additional images. In this paper, we describe the dataset and a series of laboratory- and crowd-tests we conducted to evaluate image appeal. These tests indicate that there is only a small influence when likes and views are included in the presentation of the images in comparison to when these are not shown, and also the appeal ratings are only a little correlated to likes and views. Furthermore, it is shown that lab and crowd tests are highly similar considering the collected appeal ratings. In addition to the dataset, we also describe various machine learning models for the prediction of image appeal, using only the photo itself as input. The models have a similar or slightly better performance than state-of-the-art models. The evaluation indicates that there is still an improvement in image appeal prediction and furthermore, other aspects, such as the presentation context could be evaluated.

In many research fields, human-annotated data plays an important role as it is used to accomplish a multitude of tasks. One such example is in the field of multimedia quality assessment where subjective annotations can be used to train or evaluate quality prediction models. Lab-based tests could be one approach to get such quality annotations. They are usually performed in well-defined and controlled environments to ensure high reliability. However, this high reliability comes at a cost of higher time consumption and costs incurred. To mitigate this, crowd or online tests could be used. Usually, online tests cover a wider range of end devices, environmental conditions, or participants, which may have an impact on the ratings. To verify whether such online tests can be used for visual quality assessment, we designed three online tests. These online tests are based on previously conducted lab tests as this enables comparison of the results of both test paradigms. Our focus is on the quality assessment of high-resolution images and videos. The online tests use AVrate Voyager, which is a publicly accessible framework for online tests. To transform the lab tests into online tests, dedicated adaptations in the test methodologies are required. The considered modifications are, for example, a patch-based or centre cropping of the images and videos, or a randomly sub-sampling of the to-be-rated stimuli. Based on the analysis of the test results in terms of correlation and SOS analysis it is shown that online tests can be used as a reliable replacement for lab tests albeit with some limitations. These limitations relate to, e.g., lack of appropriate display devices, limitation of web technologies, and modern browsers considering support for different video codecs and formats.