IEEE Senior Member is the highest professional grade for which an IEEE member can apply. This distinction requires extensive professional experience and demonstrated accomplishments that reflect technical expertise, leadership, and professional maturity. Fewer than 10% of IEEE’s nearly half a million members worldwide have achieved this honor.
The Association for Computing Machinery (ACM) has recognized Christian Timmerer as a Senior Member, honoring his professional achievements and contributions to the field of computing.
The ACM Senior Member designation is awarded to individuals who have demonstrated significant performance and commitment within the computing profession. This distinction highlights Christian Timmerer’s ongoing engagement with the research community and his impact on advancing the discipline.
As part of this recognition, he will receive an official ACM Senior Member certificate and pin, and his name will be listed on the ACM Senior Member award page.
Christian Timmerer also expressed his sincere appreciation to colleagues, collaborators, and supporters who contributed throughout the nomination process, emphasizing that this recognition reflects a shared effort within the community.
This honor underscores both his individual accomplishments and his continued dedication to excellence in computing research and practice.
[PDF]
Mohammad Ghasempour (AAU, Austria), Hadi Amirpour (AAU, Austria), and Christian Timmerer (AAU, Austria)
Abstract: The rise in large-scale video streaming has led to increased energy demands across the encoding, transmission, and decoding pipeline. While energy consumption in video streaming has been widely studied, encoding decisions are typically made without explicitly accounting for expected content demand. As a result, the impact of view count on energy consumption and compression efficiency remains largely unexplored. This limits the ability to make informed and efficient encoding decisions in real-world streaming scenarios. In this paper, we propose EcoEncode, an analytical framework to evaluate the impact of view count on codec-level encoding decisions and the resulting trade-offs between energy consumption and compression efficiency. We further show that these decisions depend on video content characteristics and encoding configurations. Based on our findings, we provide practical insights to guide the selection of codecs and presets. Experimental results show that view count is a key factor in codec-level decisions. For low-popularity videos, EcoEncode achieves up to 99% energy savings with only 1-4 VMAF points of quality loss. Across all scenarios, the selected configurations lie on or near the Pareto frontier, and EcoEncode improves quality by up to 14 VMAF points over the least energy-consuming configuration.
As part of the “FÄKT” initiative by the Austrian Academy of Sciences, which produces science videos for audiences aged 10 to 14, a recent episode highlights insights into the world of video streaming: Christian Timmerer, head of the Christian Doppler Laboratory for Adaptive Streaming over HTTP and Emerging Network-based Multimedia Services at the University of Klagenfurt and a two-time Technology & Engineering Emmy® Award recipient, explains how video streaming works on a global scale and how it is continuously optimized.
The short video provides an accessible overview of the technologies behind modern streaming services and demonstrates how research contributes to improving the quality and efficiency of video delivery. Please note that the video is currently available in German only.
Links
The Lange Nacht der Forschung 2026 (long night of research) turned out to be a truly special evening — one that once again demonstrated how powerful it can be to bring science and research closer to the public. Thanks to the remarkable engagement, creativity, and enthusiasm of everyone involved, complex ideas were transformed into hands-on experiences for a broad and diverse audience.
With more than 9,000 visitors across the Lakeside Science & Technology Park and the University of Klagenfurt campus, the event was a great success. Each individual station contributed to making research tangible, interactive, and inspiring.
Our department was proudly represented with six stations/booths, four of which were hosted by our lab. Together, they showcased cutting-edge research in multimedia, artificial intelligence, and interactive systems, thus demonstrating both scientific depth and real-world impact.
At our lab’s four stations, visitors had the opportunity to explore current research in an engaging and interactive way:
Detecting Damage in Wind Turbines with AI (L25)
How can we inspect wind turbines without shutting them down? This station introduced the DORBINE project, where AI-powered drone swarms are used for automated inspection. A two-meter model vividly demonstrated how such intelligent systems could reduce costs and downtime while improving energy efficiency.
Making 3D Video More Realistic (L26)
Visitors were introduced to 3D Gaussian Splatting (3DGS), a next-generation 3D video technology that enables highly realistic rendering of scenes with reduced data requirements. Through hands-on interaction, they experienced how real-world environments can be captured and reproduced as immersive 3D spaces.
Enhancing Video Quality with Super-Resolution (L27)
This station focused on AI-based super-resolution techniques. Attendees could directly compare videos of different quality levels and observe in real time how machine learning reconstructs fine details and textures from low-resolution footage.
Experiencing Multimedia with 3D Interaction (L28)
Using Apple Vision Pro head-mounted displays, visitors explored stereoscopic spatial videos and tested their skills in a 3D dart game. This station highlighted how perception and interaction merge in next-generation multimedia experiences, offering a glimpse into future human-computer interaction.
What made the evening particularly special was not only the technologies themselves but also the way they were communicated: interactive demos, hands-on exploration, and direct conversations with researchers allowed visitors of all ages to engage with science in a meaningful way.
A big thank you to everyone who contributed to making this event such a success, through preparation, creativity, and dedication on-site. Events like the Lange Nacht der Forschung thrive on teamwork, and this year was a perfect example.
[PDF]
Yiying Wei (AAU, Austria), Hadi Amirpour (AAU, Austria), Jong Hwan Ko (SKKU, South Korea), and Christian Timmerer (AAU, Austria)
Abstract: Leveraging the overfitting property of deep neural networks (DNNs) is trending in video delivery systems to enhance video quality within bandwidth limits. Existing approaches transmit overfitted super-resolution (SR) model streams for low-resolution (LR) bitstreams, which are used to reconstruct high-resolution (HR) videos at the decoder. Although these approaches show promising results, the huge computational costs of training a large number of video frames limit their practical applications. To overcome this challenge, we propose an efficient patch sampling method named EPS for video SR network overfitting, which identifies the most valuable training patches from video frames.
To this end, we first present two low-complexity Discrete Cosine Transform (DCT)-based spatial-temporal features to measure the complexity score of each patch directly. By analyzing the histogram distribution of these features, we then categorize all possible patches into different clusters and select training patches from the cluster with the highest spatial-temporal information. The number of sampled patches is adaptive based on the video content, addressing the trade-off between training complexity and efficiency.
Our method reduces the number of training patches by 75.00% to 91.69%, depending on the resolution and number of clusters, while preserving high video quality and greatly improving training efficiency. Our method speeds up patch sampling by up to 82.1× compared to the state-of-the-art patch sampling technique (EMT).
Sustainability in Video Encoding and Streaming:
Energy-Efficient Techniques and Metrics
Workshop on Media Energy Consumption Measurement and Exposure
[Workshop URL] [Slides] [PDF]
Presenter: Christian Timmerer (Alpen-Adria-Universität Klagenfurt
Abstract: The presentation discusses the increasing environmental impact of video streaming and highlights the urgent need for more sustainable approaches across the entire streaming pipeline. Video traffic dominates internet usage and contributes significantly to global greenhouse gas emissions, while the demand for higher quality content continues to drive up computational complexity and energy consumption in encoding, delivery, and playback.
A central insight is that there is a strong trade-off between video quality and energy consumption, where small reductions in quality can lead to substantial energy savings. By introducing energy as an explicit optimization objective, techniques such as content-aware encoding, energy-aware bitrate ladder construction, and real-time optimization for live streaming can significantly reduce energy usage while maintaining nearly the same perceptual quality.
The work also emphasizes the role of adaptive bitrate algorithms that incorporate energy consumption alongside traditional quality and buffer-based metrics. These approaches demonstrate that it is possible to simultaneously improve user experience and reduce energy consumption, indicating that sustainability and performance can be aligned rather than conflicting goals.
To enable such optimizations, the presentation introduces a range of metrics and models, including video complexity measures, quality prediction models, and machine learning-based approaches for estimating encoding and decoding energy as well as CO₂ emissions. These tools support more informed, data-driven decisions across the full streaming workflow from encoding to playback.
Another important theme is end-to-end optimization, where energy efficiency depends on the combined behavior of encoding strategies, bitrate selection, and client-side adaptation. Industry efforts confirm the practical relevance of these approaches and highlight the importance of collaboration and real-world validation.
Despite promising results, several challenges remain, including difficulties in measuring and benchmarking energy consumption, the lack of standardized methodologies, and the limited integration of energy considerations into existing workflows. Overall, the presentation argues that energy consumption should become a first-class optimization target in video streaming systems, similar to established quality metrics, to enable truly sustainable media delivery.
Keywords: sustainable streaming, energy-aware encoding, adaptive bitrate streaming, green multimedia, video compression, bitrate ladder optimization, QoE optimization, energy-quality tradeoff, video complexity analysis, CO2 footprint, energy modeling, machine learning for video, end-to-end optimization, eco-efficient streaming, real-time streaming optimization
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