Christian Timmerer

IEEE Communication Magazine: From Capturing to Rendering: Volumetric Media Delivery With Six Degrees of Freedom


Teaser: “Help me, Obi-Wan Kenobi. You’re my only hope,” said the hologram of Princess Leia in Star Wars: Episode IV – A New Hope (1977). This was the first time in cinematic history that the concept of holographic-type communication was illustrated. Almost five decades later, technological advancements are quickly moving this type of communication from science fiction to reality.

Authors: Jeroen van der Hooft (Ghent University), Maria Torres Vega (Ghent University), Tim Wauters (Ghent University), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Bitmovin), Ali C. Begen (Ozyegin University, Networked Media), Filip De Turck (Ghent University), and Raimund Schatz (AIT Austrian Institute of Technology)

Abstract: Technological improvements are rapidly advancing holographic-type content distribution. Significant research efforts have been made to meet the low-latency and high-bandwidth requirements set forward by interactive applications such as remote surgery and virtual reality. Recent research made six degrees of freedom (6DoF) for immersive media possible, where users may both move their heads and change their position within a scene. In this article, we present the status and challenges of 6DoF applications based on volumetric media, focusing on the key aspects required to deliver such services. Furthermore, we present results from a subjective study to highlight relevant directions for future research.

Link: IEEE Communication Magazine

Paper accepted: Automated Bank Cheque Verification Using Image Processing and Deep Learning Methods

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Authors: Prateek Agrawal (University of Klagenfurt, Austria), Deepak Chaudhary (Lovely Professional University, India), Vishu Madaan (Lovely professional University, India), Anatoliy Zabrovskiy (University of Klagenfurt, Austria), Radu Prodan (University of Klagenfurt, Austria), Dragi Kimovski (University of Klagenfurt, Austria), Christian Timmerer (University of Klagenfurt, Austria)

Abstract: Automated bank cheque verification using image processing is an attempt to complement the present cheque truncation system, as well as to provide an alternate methodology for the processing of bank cheques with minimal human intervention. When it comes to the clearance of the bank cheques and monetary transactions, this should not only be reliable and robust but also save time which is one of the major factor for the countries having large population. Read more

Paper accepted VCIP’20: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Learning


Authors: Ekrem Çetinkaya (Alpen-Adria-Universität Klagenfurt), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Bitmovin), and Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK)

Abstract: HTTP Adaptive Streaming (HAS) is the most common approach for delivering video content over the Internet. The requirement to encode the same content at different quality levels (i.e., representations) in HAS is a challenging problem for content providers. Fast multirate encoding approaches try to accelerate this process by reusing information from previously encoded representations. In this paper, we use convolutional neural networks (CNNs) to speed up the encoding of multiple representations with a specific focus on parallel encoding. In parallel encoding, the overall time-complexity is limited to the maximum time-complexity of one of the representations that are encoded in parallel. Therefore, instead of reducing the time-complexity for all representations, the highest time-complexities are reduced. Experimental results show that the proposed method achieves significant time-complexity savings in parallel encoding scenarios (41%) with a slight increase in bitrate and quality degradation compared to the HEVC reference software.

Keywords: Video Coding, Convolutional Neural Networks, HEVC, HTTP Adaptive Streaming (HAS)

Is it getting too FOGGY?

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The FOG just moved from the Lake Wörthersee to ITEC ;)! Lead researchers Dragi Kimovski, and Narges Mehran from Radu Prodan’s Lab and Josef Hammer from Hermann Hellwagner’s Lab setup UNI-KLU’s first FOG infrastructure with 40 computing nodes including 5 GPU-enabled ones.

Why should Cloud have all the FUN xD?


Alexander Lercher nominated for Best Performer Award by Faculty of Technical Sciences, UNI-KLU

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Faculty of Technical Sciences, University of Klagenfurt nominated Alexander Lercher from ITEC (Radu Prodan‘s group) for Best Performer Award owing to his outstanding performance in studies.  He will be conferred with this honor at a public presentation in lecture hall -3 of the University of Klagenfurt on September 16, 2020. In the course of research carried out by the Studies and Examination Department, Alexander was identified as the most successful student in this field of study.

Prof. Radu Prodan

Paper accepted in the Journal of Information and Software Technology (INSOF).

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Elsevier’s Journal of Information and Software Technology (INSOF) accepted the manuscript A Dynamic Evolutionary Multi-Objective Virtual Machine Placement Heuristic for Cloud Infrastructures”.

Authors: Ennio Torre, Juan J. Durillo (Leibniz Supercomputing Center), Vincenzo de Maio (Vienna University of Technology), Prateek Agrawal (University of Klagenfurt), Shajulin Benedict (Indian Institute of Information Technology), Nishant Saurabh (University of Klagenfurt), Radu Prodan (University of Klagenfurt).

Abstract: Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource over-commitment affecting the Quality of Service (QoS) of the running applications. The effective trade-off between resource wastage and over-commitment is a challenging task in virtualized Clouds and depends on the allocation of virtual machines (VMs) to physical resources. We propose in this paper a multi-objective method for dynamic VM placement, which exploits live migration mechanisms to simultaneously optimize the resource wastage, over-commitment ratio and migration energy. Our optimization algorithm uses a novel evolutionary meta-heuristic based on an island population model to approximate the Pareto optimal set of VM placements with good accuracy and diversity. Simulation results using traces collected from a real Google cluster demonstrate that our method outperforms related approaches by reducing the migration energy by up to 57 % with a QoS increase below 6 %.


This work is supported by:

  • European Union’s Horizon 2020 research and innovation programme, grant agreement 825134, “Smart Social Media Ecosytstem in a Blockchain Federated Environment (ARTICONF)”;
  • Austrian Science Fund (FWF), grant agreement Y 904 START-Programm 2015, “Runtime Control in Multi Clouds (RUCON)“;
  • Austrian Agency for International Cooperation in Education and Research (OeAD-GmbH) and Indian Department of Science and Technology (DST), project number, IN 20/2018, “Energy Aware Workflow Compiler for Future Heterogeneous Systems”.
Nishant Saurabh

Paper accepted in the Journal of Parallel and Distributed Computing (JPDC)

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The manuscript ”Expelliarmus: Semantic-Centric Virtual Machine Image Management in IaaS Clouds” is accepted for publication at the Journal of Parallel and Distributed Computing (JPDC) (

Authors: Nishant Saurabh (University of Klagenfurt), Shajulin Benedict (Indian Institute of Information Technology, Kottayam), Jorge G. Barbosa (LIACC, Faculdade de Engenharia da Universidade do Porto), Radu Prodan (University of Klagenfurt).

Abstract: Infrastructure-as-a-service (IaaS) Clouds concurrently accommodate diverse sets of user requests, requiring an efficient strategy for storing and retrieving virtual machine images (VMIs) at a large scale. The VMI storage management require dealing with multiple VMIs, typically in the magnitude of gigabytes, which entails VMI sprawl issues hindering the elastic resource management and provisioning. Nevertheless, existing techniques to facilitate VMI management overlook VMI semantics (i.e at the level of base image and software packages) with either restricted possibility to identify and extract reusable functionalities or with higher VMI publish and retrieval overheads. In this paper, we design, implement and evaluate Expelliarmus, a novel VMI management system that helps to minimize storage, publish and retrieval overheads. To achieve this goal, Expelliarmus incorporates three complementary features. First, it makes use of VMIs modelled as semantic graphs to expedite the similarity computation between multiple VMIs. Second, Expelliarmus provides a semantic aware VMI decomposition and base image selection to extract and store non-redundant base image and software packages. Third, Expelliarmus can also assemble VMIs based on the required software packages upon user request. We evaluate Expelliarmus through a representative set of synthetic Cloud VMIs on the real test-bed. Experimental results show that our semantic-centric approach is able to optimize repository size by 2.3-22 times compared to state-of-the-art systems (e.g. IBM’s Mirage and Hemera) with significant VMI publish and slight retrieval performance improvement.


This work is supported by:

  • European Union’s Horizon 2020 research and innovation programme, grant agreement 825134, “Smart Social Media Ecosytstem in a Blockchain Federated Environment (ARTICONF)”;
  • Austrian Agency for International Cooperation in Education and Research (OeAD-GmbH) and Indian Department of Science and Technology (DST), project number, IN 20/2018, “Energy Aware Workflow Compiler for Future Heterogeneous Systems”
Christian Timmerer

QUALINET announces its recent White Paper on Definitions of Immersive Media Experience (IMEx)


With the coming of age of virtual/augmented reality and interactive media, numerous definitions, frameworks, and models of immersion have emerged across different fields ranging from computer graphics to literary works. Immersion is oftentimes used interchangeably with presence as both concepts are closely related. However, there are noticeable interdisciplinary differences regarding definitions, scope, and constituents that are required to be addressed so that a coherent understanding of the concepts can be achieved. Such consensus is vital for paving the directionality of the future of immersive media experiences (IMEx) and all related matters. Read more

ACM Multimedia’20: Relevance-Based Compression of Cataract Surgery Videos Using Convolutional Neural Networks


Authors: Negin Ghamsarian (Alpen-Adria-Universität Klagenfurt), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Bitmovin), Mario Taschwer (Alpen-Adria-Universität Klagenfurt), and Klaus Schöffmann (Alpen-Adria-Universität Klagenfurt)

Abstract: Recorded cataract surgery videos play a prominent role in training and investigating the surgery, and enhancing the surgical outcomes. Due to storage limitations in hospitals, however, the recorded cataract surgeries are deleted after a short time and this precious source of information cannot be fully utilized. Lowering the quality to reduce the required storage space is not advisable since the degraded visual quality results in the loss of relevant information that limits the usage of these videos. To address this problem, we propose a relevance-based compression technique consisting of two modules: (i) relevance detection, which uses neural networks for semantic segmentation and classification of the videos to detect relevant spatio-temporal information, and (ii) content-adaptive compression, which restricts the amount of distortion applied to the relevant content while allocating less bitrate to irrelevant content. The proposed relevance-based compression framework is implemented considering five scenarios based on the definition of relevant information from the target audience’s perspective. Experimental results demonstrate the capability of the proposed approach in relevance detection. We further show that the proposed approach can achieve high compression efficiency by abstracting substantial redundant information while retaining the high quality of the relevant content.

ACM International Conference on Multimedia 2020, Seattle, United States.


Keywords: Video Coding, Convolutional Neural Networks, HEVC, ROI Detection, Medical Multimedia.

BigMM’20: ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Sequences using Artificial Neural Network


The Sixth IEEE International Conference on Multimedia Big Data (BigMM 2020)

Authors: Anatoliy Zabrovskiy (Alpen-Adria-Universitat Klagenfurt), Prateek Agrawal (Alpen-Adria-Universitat Klagenfurt, Lovely Professional University), Roland Matha (Alpen-Adria-Universitat Klagenfurt), Christian Timmerer (Alpen-Adria-Universitat Klagenfurt, Bitmovin) and Radu Prodan (Alpen-Adria-Universitat Klagenfurt).

Abstract: HTTP Adaptive Streaming of video content is becoming an integral part of the Internet and accounts for the majority of today’s traffic. Although Internet bandwidth is constantly increasing, video compression technology plays an important role and the major challenge is to select and set up multiple video codecs, each with hundreds of transcoding parameters. Additionally, the transcoding speed depends directly on the selected transcoding parameters and the infrastructure used. Predicting transcoding time for multiple transcoding parameters with different codecs and processing units is a challenging task, as it depends on many factors. This paper provides a novel and considerably fast method for transcoding time prediction using video content classification and neural network prediction. Our artificial neural network (ANN) model predicts the transcoding times of video segments for state-of-the-art video codecs based on transcoding parameters and content complexity. We evaluated our method for two video codecs/implementations (AVC/x264 and HEVC/x265) as part of large-scale HTTP Adaptive Streaming services. The ANN model of our method is able to predict the transcoding time by minimizing the mean absolute error (MAE) to 1.37 and 2.67 for x264 and x265 codecs, respectively. For x264, this is an improvement of 22% compared to the state of the art.

Keywords: Transcoding time prediction, adaptive streaming, video transcoding, neural networks, video encoding, video complexity class, MPEG-DASH