Publications

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.

Augmented Reality (AR) and Virtual Reality (VR) are pushing from the labs towards consumers, especially with social applications. These applications require visual representations of humans and intelligent entities. However, displaying and animating photo-realistic models comes with a high technical cost while low-fidelity representations may evoke eeriness and overall could degrade an experience. Thus, it is important to carefully select what kind of avatar to display. This article investigates the effects of rendering style and visible body parts in AR and VR by adopting a systematic literature review. We analyzed 72 papers that compare various avatar representations. Our analysis includes an outline of the research published between 2015 and 2022 on the topic of avatars and agents in AR and VR displayed using head-mounted displays, covering aspects like visible body parts (e.g., hands only, hands and head, full-body) and rendering style (e.g., abstract, cartoon, realistic); an overview of collected objective and subjective measures (e.g., task performance, presence, user experience, body ownership); and a classification of tasks where avatars and agents were used into task domains (physical activity, hand interaction, communication, game-like scenarios, and education/training). We discuss and synthesize our results within the context of today's AR and VR ecosystem, provide guidelines for practitioners, and finally identify and present promising research opportunities to encourage future research of avatars and agents in AR/VR environments.

In this column, we reflect on the environmental impact and broader sustainability implications of resource-demanding digital applications and services such as video streaming, VR/AR/XR and videoconferencing. We put emphasis not only on the experiences and use cases they enable but also on the "cost" of always striving for high Quality of Experience (QoE) and better user experiences. Starting by sketching the broader context, our aim is to raise awareness about the role that QoE research can play in the context of various of the United Nations' Sustainable Development Goals (SDGs), either directly (e.g., SDG 13 "climate action") or more indirectly (e.g., SDG 3 "good health and well-being" and SDG 12 "responsible consumption and production").