The Institute of Information Technology at the
Alpen-Adria-Universität Klagenfurt
invites applications for:
Doctoral Student Positions (100% employment; all genders welcome)
within the Christian Doppler (CD) Pilot Laboratory ATHENA
“Adaptive Streaming over HTTP and
Emerging Networked Multimedia Services”
The expected start date of employment is April 1st, 2023.
Application deadline: December 1st, 2022.
Find the complete job description here.
Come and join our ATHENA team!
IEEE 24th Workshop on MultiMedia Signal Processing (MMSP)
September 26-28, 2022 | Shanghai, China
Lingfeng Qu (Southwest Jiaotong University), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Mohammad Ghanbari (University of Essex, UK), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt), Hongjie He (Southwest Jiaotong University)
Abstract: This paper introduces an algorithm to improve the security, efficiency, and
embedding capacity of reversible data hiding in encrypted images (RDH-EI). It is based on
classification encryption difference and adaptive fixed-length coding. Firstly, the prediction error image is obtained, the difference with a bin value greater than the encryption threshold in the difference histogram is found, and it is further modified to obtain the embedding threshold range. Then, under the condition of ensuring that the difference inside and outside the threshold range is not confused, the difference within the threshold is only scrambled, and the difference outside the threshold is scrambled and mod encrypted. After obtaining the encrypted image, an adaptive difference fixed-length coding method is proposed to encode and compress the differences within the threshold. 
The secret data is embedded in the multiple most significant bits of the encoded difference. Experimental results show that the embedding capacity of the proposed algorithm is improved compared with the state-of-the-art algorithm.
October 10-12, 2022 | Thessaloniki, Greece
Jesús Aguilar Armijo (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt) and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)
Abstract: Segment prefetching is a technique that consists of sending the next segment(s) in advance closer to the user to serve content with reduced latency. Due to its location and capabilities, an edge computing node is an ideal component for executing
segment prefetching policies and storing/caching the prefetched segments. In this work, we study segment prefetching techniques deployed at the edge computing node for adaptive video streaming. We propose different types of segment prefetching policies
and study their costs and benefits, including segment prefetching based on past segment requests, transrating, a Markov prediction model, and machine learning. Besides, we analyze which segment prefetching policy is better under which circumstances, and the
influence of the ABR algorithm and the bitrate ladder on segment prefetching.
Keywords: Edge computing, MEC, content delivery, adaptive video streaming, HAS, segment prefetching
In July-August 2022, the ATHENA Christian Doppler Laboratory hosted four interns working on the following topics:
At the end of their internships, they presented their works and achieved results, and received official certificates from the university. We believe the joint work with them was beneficial for both the laboratory and the interns. We would like to thank the interns for their genuine interest, productive work, and excellent feedback about our laboratory.
Fabio Zinner: “my four weeks, I had an amazingly practical and theoretical experience which is very important for my future practical and academic line of work! It was great and fascinating working with Python, Mininet, Linux, FFMpeg, Gpac, Iperf, etc. I really liked working with ATHENA, and the experience I gathered was exceptional. Also, I am very happy that I had Reza Farahani as my supervisor!”
Per-Luca Thalmann:“I really enjoyed my 4 weeks at ATHENA. At first, I had to read a lot of articles and papers to get a basic understanding of Video Codecs and encoding. As I started my Main Project, which evaluated the performance of modern codecs with different video complexities, I noticed that everything I had read before was useful to progress faster towards my end goal. After I got the results of my script, which ran for over a week, I also noticed some outcomes which were not expected. Basically, that an older codecs get at some very specific settings higher Scores than his successor. Whenever I got stuck or had any questions, my supervisor, Vignesh, helped me. I did not only improve my technical knowledge, I also got a lot insights into how research works, what is the motivation of research and also about the process for scientific research.”
Georg Kelih:“I worked by Athena as an Intern for a month and got the tasks to build a simulator which simulates the server client communication (ABR, bitrate ladder, resource allocation) and shows the results in a graph and a Server Client script where the server runs on the local host and the client requests segments and plays them using python-vlc
My daily routine was pretty chill, not only because we had only 30 hours to work, but also the programming was quite fun and challenging. So my day looked something like this I stand up go to work play a round table soccer and then start to work start Visual Studio Code and write the code I thought about yesterday hope that it runs, but it shows you just a few error messages start debugging then notice that it’s already time to eat something and that I am hungry, eat something and find finally after your lunch the silly error I made think about new implementation and better ways to solve something and then it is already time to go, so you go to the strandbad to swim a round and then drive home. Something like this, my daily routine looked like. For me, I think it was a bit too chill for my taste because I like the stress of a 40-hour week especially when I only work in my holidays.
But the rest was absolutely nice, especially that here by Athena are so many people from different countries is pretty cool. For my self, I learned not many new skills, but I found out about many new Linux tools and how to find information even more efficiently.”
December 4-8, 2022 |Rio de Janeiro, Brazil
Reza Farahani (Alpen-Adria-Universität Klagenfurt, Austria), Abdelhak Bentaleb (National University of Singapore, Singapore), Ekrem Cetinkaya (Alpen-Adria-Universität Klagenfurt, Austria), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria), Roger Zimmermann (National University of Singapore, Singapore), and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt, Austria)
Abstract: a cost-effective, scalable, and flexible architecture that supports low latency and high-quality live video streaming is still a challenge for Over-The-Top (OTT) service providers. To cope with this issue, this paper leverages Peer-to-Peer (P2P), Content Delivery Network (CDN), edge computing, Network Function Virtualization (NFV), and distributed video transcoding paradigms to introduce a hybRId P2P-CDN arcHiTecture for livE video stReaming (RICHTER). We first introduce RICHTER’s multi-layer architecture and design an action tree that considers all feasible resources provided by peers, edge, and CDN servers for serving peer requests with minimum latency and maximum quality. We then formulate the problem as an optimization model executed at the edge of the network. We present an Online Learning (OL) approach that leverages an unsupervised Self Organizing Map (SOM) to (i) alleviate the time complexity issue of the optimization model and (ii) make it a suitable solution for large-scale scenarios by enabling decisions for groups of requests instead of for single requests. Finally, we implement the RICHTER framework, conduct our experiments on a large-scale cloud-based testbed including 350 HAS players, and compare its effectiveness with baseline systems. The experimental results illustrate that RICHTER outperforms baseline schemes in terms of users’ Quality of Experience (QoE), latency, and network utilization, by at least 59%, 39%, and 70%, respectively.
Index Terms—HAS; Edge Computing; NFV; CDN; P2P; Low Latency; QoE; Video Transcoding; Online Learning.
September 5-7, 2022 | Lippstadt, Germany
Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Raimund Schatz (AIT Austrian Institute of Technology, Austria), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt)
Abstract:
We currently witness the rapidly growing importance of intelligent video streaming quality 
optimization and reduction of video delivery costs. Per-Title encoding, in contrast to a fixed bitrate ladder, shows significant promise to deliver higher quality video streams by addressing the trade-off between compression efficiency and video characteristics such as resolution and frame rate.
Selecting encodings with noticeable quality differences in between prevents the construction of an inefficient bitrate ladder that suffers from too similar quality representations.
In this respect, the VMAF metric represents a promising foundation for bitrate laddering, as it currently yields the highest video quality prediction performance. However, the minimum noticeable quality difference, referred as to just-noticeable-difference (JND), has not been properly validated for VMAF yet, with existing sources proposing highly diverse ΔVMAF step sizes ranging from two to six.
10th European Workshop on Visual Information Processing (EUVIP)
September 11-14, 2022 | Lisbon, Portugal
Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Christine Guillemot (INRIA, France), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt)
Abstract:
Light fields are typically represented by multi-view images and enable post-capture
actions such as refocusing and perspective shift. To compress a light field image, its view images are typically converted into a pseudo video sequence (PVS) and the generated PVS is compressed using a video codec. However, when using the inter-coding tool of a video codec to exploit the redundancy among view images, the possibility to randomly access any view image is lost. On the other hand, when video codecs independently encode view images using the intra-coding tool, random access to view images is enabled, however, at the expense of a significant drop in the compression efficiency. To address this trade-off, we propose to use neural representations to represent 4D light fields. For each light field, a multi-layer perceptron (MLP) is trained to map the light field four dimensions to the color space, thus enabling random access even to pixels. To achieve higher compression efficiency, neural network compression techniques are deployed. The proposed method outperforms the compression efficiency of HEVC inter-coding, while providing random access to view images and even pixel values.
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