On Monday, 16 June 2025, Sabrina Größing, BEd and Dr Felix Schniz presented the Master’s Programme Game Studies and Engineering to an audience of bachelor students from the novel Liberal Arts programme.
The event aimed to showcase how easily they can continue their academic career within AAU, the opportunities and support structures awaiting them by doing so, and provide a gateway into studying at the university’s technical faculty.
On Friday, 13 June 2025, The Pioneers of Game Development Austria, the game development association focused on supporting, showcasing, and accelerating Austrian games, developers, and business, visited ITEC.
In an event organised by Dr Felix Schniz, Martin Filipp (Mi’pu’mi Games and PGDA representative) brought a delegation of industry representatives to campus, including Michael Benda (Zeppelin Studio), Raffael Moser (reignite games), and Manuel Bonell (Immerea). They held presentations on the status quo of the Austrian games industry and entertained a developer café in the afternoon, where students could sign up for discussion slots to ask questions about how to get into the industry and how to start your own gaming studio.
The event was visited by over 40 visitors, most of them students of the master’s programme Game Studies and Engineering.
My dear colleagues,
The PGDA, the Austrian Game Developers Association, is going to visit us with a delegation
On Friday, 13 June, between 11.45am and 06.00pm in S.2.42, they are going to offer talks and opportunities for individual Developer Café chat sessions.
The schedule looks as follows:
11.45am: Room Opens
12.00pm: Introduction and PGDA session on the Austrian game industry today
12.45pm: PGDA talks on various topics, including genre
13.30pm: Break
02.00pm: Developer Café
06.00pm: Ending
You can register for a time slot to meet with and talk to one of the developers visiting us on the day of the event.
We are looking forward to seeing you there!
All the best,
Felix
EUVIP 2025
October 13-16, 2025
Malta
Tutorial speakers:
Tutorial description:
As multimedia services like video streaming, video conferencing, virtual reality (VR), and online gaming continue to evolve, ensuring high perceptual visual quality is crucial for enhancing user experience and maintaining competitiveness. However, multimedia content inevitably undergoes various distortions during acquisition, compression, transmission, and storage, leading to quality degradation. Therefore, perceptual visual quality assessment, which evaluates multimedia quality from a human perception perspective, plays a vital role in optimizing user experience in modern communication systems. This tutorial provides a comprehensive overview of perceptual visual quality assessment, covering both subjective methods, where human observers directly rate their experience, and objective methods, where computational models predict perceptual quality based on measurable factors such as bitrate, frame rate, and compression levels. The session also explores quality assessment metrics tailored to different types of multimedia content, including images, videos, VR, point clouds, meshes, and AI-generated media. Furthermore, we discuss challenges posed by diverse multimedia characteristics, complex distortion scenarios, and varying viewing conditions. By the end of this tutorial, attendees will gain a deep understanding of the principles, methodologies, and latest advancements in perceptual visual quality assessment for multimedia communication.
Machine Learning-Based Decoding Energy Modeling for VVC Streaming
2025 IEEE International Conference on Image Processing (ICIP)
14-17 September, Anchorage, Alaska, USA
Reza Farahani (AAU Klagenfurt, Austria), Vignesh V Menon (Fraunhofer HHI, Germany), and Christian Timmerer (AAU Klagenfurt, Austria)
Abstract: Efficient video streaming requires jointly optimizing encoding parameters (bitrate, resolution, compression efficiency) and decoding constraints (computational load, energy consumption) to balance quality and power efficiency, particularly for resource-constrained devices. However, hardware heterogeneity, including differences in CPU/GPU architectures, thermal management, and dynamic power scaling, makes absolute energy models unreliable, particularly for predicting decoding consumption. This paper introduces the Relative Decoding Energy Index (RDEI), a metric that normalizes decoding energy consumption against a baseline encoding configuration, eliminating device-specific dependencies to enable cross-platform comparability and guide energy-efficient streaming adaptations. We use a dataset of 1000 video sequences to extract complexity features capturing spatial and temporal variations, employ Versatile Video Coding (VVC) open-source toolchain using VVenC/VVdeC with various resolutions, framerate, encoding preset and quantization parameter (QP) sets, and model RDEI using Random Forest (RF), XGBoost, Linear Regression (LR), and Shallow Neural Networks (NN) for decoding energy prediction. Experimental results demonstrate that RDEI-based predictions provide accurate decoding energy estimates across different hardware, ensuring cross-device comparability in VVC streaming.
Keywords: Video Streaming; Energy Prediction; Versatile Video Coding (VVC); Video Complexity Analysis.
co-located with ICCV 2025
ICCV 2025 Workshop: Visual Quality Assessment Competition | ATHENA Christian Doppler (CD) Laboratory
Visual quality assessment plays a crucial role in computer vision, serving as a fundamental step in tasks such as image quality assessment (IQA), image super-resolution, document image enhancement, and video restoration. Traditional visual quality assessment techniques often rely on scalar metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), which, while effective in certain contexts, fall short in capturing the perceptual quality experienced by human observers. This gap emphasizes the need for more perceptually aligned and comprehensive evaluation methods that can adapt to the growing demands of applications such as medical imaging, satellite remote sensing, immersive media, and document processing. In recent years, advancements in deep learning, generative models, and multimodal large language models (MLLMs) have opened up new avenues for visual quality assessment. These models offer capabilities that extend beyond traditional scalar metrics, enabling more nuanced assessments through natural language explanations, open-ended visual comparisons, and enhanced context awareness. With these innovations, VQA is evolving to better reflect human perceptual judgments, making it a critical enabler for next-generation computer vision applications.
The VQualA Workshop aims to bring together researchers and practitioners from academia and industry to discuss and explore the latest trends, challenges, and innovations in visual quality assessment. We welcome original research contributions addressing, but not limited to, the following topics: