Authors: Reza Farahani (AAU, Austria), and Vignesh V Menon (Fraunhofer HHI, Berlin, Germany)
Venue: The 12th European Workshop on Visual Information Processing (EUVIP 2024)
08-11 September, 2024 in Geneva, Switzerland
The 15th ACM Multimedia Systems Conference was held from 15-18 April, 2024 in Bari, Italy. MMSys 2024 provides a forum to leading researchers from academia and industry to present and share their latest findings in multimedia systems.
Christian Timmerer, Mathias Lux, Samira Afzal, Christian Bauer, Daniele Lorenzi, Emanuele Artioli, Mohammad Ghasempour, Shivi Vats, and Armin Lachini participated and presented ATHENA, GAIA, and SPIRIT contributions:
- E. Artioli, F. Tashtarian, and C. Timmerer, DIGITWISE: Digital Twin-based Modeling of Adaptive Video Streaming Engagement, Research Track
- H. Amirpour, M. Ghasempour, L. Qu, W. Hamidouche, and C. Timmerer, EVCA: Enhanced Video Complexity Analyzer, Open-source Software and Datasets
- C. Bauer, S. Afzal, S. Linder, R. Prodan, and C. Timmerer, GREEM: An Open-Source Benchmark Tool Measuring the Environmental Footprint of Video Streaming, Open-source Software and Datasets
- S. Linder, S. Afzal, C. Bauer, H. Amirpour, R. Prodan, and C. Timmerer, VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances, Open-source Software and Datasets
- F. Tashtarian, D. Lorenzi, H. Amirpour, S. Afzal, and C. Timmerer, COCONUT: Content Consumption Energy Measurement Dataset for Adaptive Video Streaming, Open-source Software and Datasets
- M. Nguyen, S. Vats, X. Zhou, I. Viola, P. Cesar, C. Timmerer, H. Hellwagner, ComPEQ–MR: Compressed Point Cloud Dataset with Eye Tracking and Quality Assessment in Mixed Reality, Open-source Software and Datasets
- S. Radler , L. Prüller, E. Artioli, F. Tashtarian, and C. Timmerer, PyStream: Enhancing Video Streaming Evaluation, Technical Demos
- E. Artioli, Generative AI for Adaptive Video Streaming, Doctoral Symposium
- M. Hoi, A. Lachini, S. Afzal, S.Linder, F. Tashtarian, R. Prodan, and C. Timmerer, VEEP: Video Encoding Energy and CO2 Emission Prediction, GMSys 2024
Within the Organizing Committee Christian Timmerer officiated as TPC Chair and Farzad Tashtarian as Proceeding Chair.
On Friday, 12 April 2024, seven representatives of the Pioneers of Game Development Austria (https://pgda.at/) visited the University of Klagenfurt for the event Press Start: Your Journey into Game Development, organised by the master’s programme Game Studies and Engineering. The PGDA members, composed of different video game developers from all over Austria, provided insightful talks, gave feedback on student game projects, and provided personal support during a mentoring café. Attracting about 50 GSE students, academic staff, senate members, and many potential new students, the event can be considered the most successful in recent GSE history.
Wenn Tom Tuček über die Welt spricht, muss er stets konkretisieren: Handelt es sich um die reale Welt oder um virtuelle Welten? Der Doktorand am Institut für Informationstechnologie beschäftigt sich aktuell mit digital humans, also virtuellen Figuren, denen wir beispielsweise in Videospielen begegnen. Tom Tuček möchte gerne wissen, wie sich der Kontakt mit digitalen Menschen, die mit neuer Künstlicher Intelligenz ausgestattet werden, auf die Spieler:innen auswirkt.
Read the whole interview here: https://www.aau.at/blog/das-spiel-mit-dem-digitalen-menschen/
The enduring popularity of the Pokémon franchise can be explained by a mix of nostalgia, constant innovation, and their appeal to a wide and diverse fan base, as Felix Schniz, game studies scholar and senior scientist at the University of Klagenfurt, explains: Pokémon is a pioneer of this dynamic. The game perfectly shows how a franchise can stay fresh and relevant through the ongoing reinterpretation of its genre dimensions.
Read the whole interview here: Dringel, Severin: “Tag des Pokémon – Warum Pokémon 28 Jahre später immer noch ein Renner ist.” Kleine Zeitung International, 27.02.2024. https://www.kleinezeitung.at/international/18047876/warum-pokemon-auch-nach-zwanzig-jahren-immer-noch-ein-renner-ist
The 15th ACM Multimedia Systems Conference (Technical Demos)
15-18 April, 2024 in Bari, Italy
Authors: Samuel Radler* (AAU, Austria) , Leon Prüller* (AAU, Austria), Emanuele Artioli (AAU, Austria), Farzad Tashtarian (AAU, Austria), and Christian Timmerer (AAU, Austria)
As streaming services become more commonplace, analyzing their behavior effectively under different network conditions is crucial. This is normally quite expensive, requiring multiple players with different bandwidth configurations to be emulated by a powerful local machine or a cloud environment. Furthermore, emulating a realistic network behavior or guaranteeing adherence to a real network trace is challenging. This paper presents PyStream, a simple yet powerful way to emulate a video streaming network, allowing multiple simultaneous tests to run locally. By leveraging a network of Docker containers, many of the implementation challenges are abstracted away, keeping the resulting system easily manageable and upgradeable. We demonstrate how PyStream not only reduces the requirements for testing a video streaming system but also improves the accuracy of the emulations with respect to the current state-of-the-art. On average, PyStream reduces the error between the original network trace and the bandwidth emulated by video players by a factor of 2-3 compared to Wondershaper, a common network traffic shaper in many video streaming evaluation environments. Moreover, PyStream decreases the cost of running experiments compared to existing cloud-based video streaming evaluation environments such as CAdViSE.
The 15th ACM Multimedia Systems Conference (Open-source Software and Datasets)
15-18 April, 2024 in Bari, Italy
Authors: Farzad Tashtarian∗ (AAU, Austria), Daniele Lorenzi∗ (AAU, Austria), Hadi Amirpour (AAU, Austria), Samira Afzal (AAU, Austria), and Christian Timmerer (AAU, Austria)
HTTP Adaptive Streaming (HAS) has emerged as the predominant solution for delivering video content on the Internet. The urgency of the climate crisis has accentuated the demand for investigations into the environmental impact of HAS techniques. In HAS, clients rely on adaptive bitrate (ABR) algorithms to drive the quality selection for video segments. Focusing on maximizing video quality, these algorithms often prioritize maximizing video quality under favorable network conditions, disregarding the impact of energy consumption. To thoroughly investigate the effects of energy consumption, including the impact of bitrate and other video parameters such as resolution and codec, further research is still needed. In this paper, we propose COCONUT, a COntent COnsumption eNergy measUrement daTaset for adaptive video streaming collected through a digital multimeter on various types of client devices, such as laptop and smartphone, streaming MPEG-DASH segments.
5-19 July, 2024, Niagra Falls, Canada
The first workshop on Surpassing Latency Limits in Adaptive Live Video Streaming (LIVES 2024) aims to bring together researchers and developers to satisfy the data-intensive processing requirements and QoE challenges of live video streaming applications through leveraging heuristic and learning-based approaches.
Delivering video content from a video server to viewers over the Internet is time-consuming in the streaming workflow and has to be handled to offer an uninterrupted streaming experience. The end-to-end latency, i.e., from the camera capture to the user device, particularly problematic for live streaming. Some streaming-based applications, such as virtual events, esports, online learning, gaming, webinars, and all-hands meetings, require low latency for their operation. Video streaming is ubiquitous in many applications, devices, and fields. Delivering high Quality-of-Experience (QoE) to the streaming viewers is crucial, while the requirement to process a large amount of data to satisfy such QoE cannot be handled with human-constrained possibilities. Satisfying the requirements of low latency video streaming applications require the streaming workflow to be optimized and streamlined all together, that includes: media provisioning (capturing, encoding, packaging, an ingesting to the origin server), media delivery (from the origin to the CDN and from the CDN to the end users), media playback (end user video player).
Please click here for more information.
On February 1st, 2024, Sahar Nasirihaghighi presented our work on ‘Event Recognition in Laparoscopic Gynecology Videos with Hybrid Transformers’ at this year’s International Conference on Multimedia Modeling (MMM 2024) in Amsterdam, The Netherlands.
Authors: Sahar Nasirihaghighi, Negin Ghamsarian, Heinrich Husslein, Klaus Schoeffmann
Abstract: Analyzing laparoscopic surgery videos presents a complex and multifaceted challenge, with applications including surgical training, intra-operative surgical complication prediction, and post-operative surgical assessment. Identifying crucial events within these videos is a significant prerequisite in a majority of these applications. In this paper, we introduce a comprehensive dataset tailored for relevant event recognition in laparoscopic gynecology videos. Our dataset includes annotations for critical events associated with major intra-operative challenges and post-operative complications. To validate the precision of our annotations, we assess event recognition performance using several CNN-RNN architectures. Furthermore, we introduce and evaluate a hybrid transformer architecture coupled with a customized training-inference framework to recognize four specific events in laparoscopic surgery videos. Leveraging the Transformer networks, our proposed architecture harnesses inter-frame dependencies to counteract the adverse effects of relevant content occlusion, motion blur, and surgical scene variation, thus significantly enhancing event recognition accuracy. Moreover, we present a frame sampling strategy designed to manage variations in surgical scenes and the surgeons’ skill level, resulting in event recognition with high temporal resolution. We empirically demonstrate the superiority of our proposed methodology in event recognition compared to conventional CNN-RNN architectures through a series of extensive experiments.
