The Graph-Massivizer Project is glad to announce that their research expands beyond the European borders. The website receives continuous visits from colleagues from US, Canada, China…and the project team is happy to develop connections with top researchers in #graphprocessing wherever they are.
In such context, Radu Prodan presented Graph-Massivizer Project at the Indian Institute of Science (IISc) in #Bangalore, at the invitation of Prof. Yogesh Simmhan.
Authors: Hadi Amirpour (AAU, Austria), Mohammad Ghasempour (AAU, Austria), Farzad Tashtarian (AAU, Austria), Ahmed Telili (TII, UAE), Samira Afzal (AAU, Austria), Wassim Hamidouche (INSA, France), Christian Timmerer (AAU, Austria)
Conference: IEEE Visual Communications and Image Processing (IEEE VCIP 2024) – Tokyo, Japan, December 8-11, 2024
Abstract: In the field of video streaming, the optimization of video encoding and decoding processes is crucial for delivering high-quality video content. Given the growing concern about carbon dioxide emissions, it is equally necessary to consider the energy consumption associated with video streaming. Therefore, to take advantage of machine learning techniques for optimizing video delivery, a dataset encompassing the energy consumption of the encoding and decoding process is needed. This paper introduces a comprehensive dataset featuring diverse video content, encoded and decoded using various codecs and spanning different devices. The dataset includes 1000 videos encoded with four resolutions (2160p, 1080p, 720p, and 540p) at two frame rates (30 fps and 60 fps), resulting in eight unique encodings for each video. Each video is further encoded with four different codecs — AVC (libx264), HEVC (libx265), AV1 (libsvtav1), and VVC (VVenC) — at four quality levels defined by QPs of 22, 27, 32, and 37. In addition, for AV1, three additional QPs of 35, 46, and 55 are considered. We measure both encoding and decoding time and energy consumption on various devices to provide a comprehensive evaluation, employing various metrics and tools. Additionally, we assess encoding bitrate and quality using quality metrics such as PSNR, SSIM, MS-SSIM, and VMAF. All data and the reproduction commands and scripts have been made publicly available as part of the dataset, which can be used for various applications such as rate and quality control, resource allocation, and energy-efficient streaming.
Dataset URL: https://github.com/cd-athena/MVCD
Index Terms— Video encoding, decoding, energy, complexity, quality.
Authors: Annalisa Gallina (UNIPD, Italy), Hadi Amirpour (AAU, Austria), Sara Baldoni (UNIPD, Italy), Giuseppe Valenzise (UPSaclay, France), Federica Battisti (UNIPD, Italy).
Conference: IEEE Visual Communications and Image Processing (IEEE VCIP 2024) – Tokyo, Japan, December 8-11, 2024
Abstract: Measuring the complexity of visual content is crucial in various applications, such as selecting sources to test processing algorithms, designing subjective studies, and efficiently determining the appropriate encoding parameters and bandwidth allocation for streaming. While spatial and temporal complexity measures exist for 2D videos, a geometric complexity measure for 3D content is still lacking. In this paper, we present the first study to characterize the geometric complexity of 3D point clouds. Inspired by existing complexity measures, we propose several compression-based definitions of geometric complexity derived from the rate-distortion curves obtained by compressing a dataset of point clouds using G-PCC. Additionally, we introduce density-based and geometry-based descriptors to predict complexity. Our initial results show that even simple density measures can accurately predict the geometric complexity of point clouds.
Index Terms— Point cloud, complexity, compression, G-PCC.
Haleh gave a presentation at the first International Workshop on Scaling Knowledge Graphs for Industry (co-located with the 20th International Conference on Semantic Systems – SEMANTICS 2024) – in Amsterdam, Sept. 17-19, 2024.
Title: “Modeling and Generating Extreme Volumes of Financial Synthetic Time-Series Data with Knowledge Graphs”
Authors: Laurentiu Vasiliu, S. Haleh S. Dizaji , Aaron Eberhart, Dumitru Roman and Radu Prodan
CERCIRAS (CA19135), short for Connecting Education and Research Communities for an Innovative Resource Aware Society is a 4-year long COST Action, started at the end of September 2020 and now nearing completion. One of the highlights of CERCIRAS has been its yearly Training School, a week-long residential school designed for current and future PhD students with research interests falling within the thematic scope of the Action and open to other profiles, including industry professionals. While perturbed by unfortunate overlap with the outbreak of the COVID pandemic, CERCIRAS managed to execute three consecutive Training Schools successfully: mid-September 2022 in Split (HR); early September 2023 in Riga (LV); and late August 2024 in Klagenfurt (AT). All editions of the CERCIRAS Training School follow a common structure: 4 selected lecture topics, which alternate frontal lessons and assisted hands-on works; 25 to 35 participants from as many participating countries as possible, with rich diversity for provenance, seniority level, and research focus; a rich portfolio of social activities.
The Training School places a considerable burden on the local hosts, which includes securing comfortable and affordable accommodation for a large troop, providing a modern and spacious lecture hall for all lectures and labs, with refreshments for the two daily breaks and nearby canteens for lunch, and arranging exciting options for a whole-afternoon diversion.
Dragi Kimovski recently visited Mother Theresa University in Skopje as part of the OeAD 6G Continuum project, which focuses on developing middleware for Artificial Intelligence over 6G networks. This visit is the next step in the collaboration between the involved institutions. During the stay, the kick-off meeting for the 6G Continuum project took place, bringing together researchers from both institutions to discuss the project’s goals and clarify the the research activities in terms of supporting AI traning and inference in the Edge over 6G networks. In addition to the research focus, this visit also helped to strengthen the ongoing partnership between the institutions. They explored new opportunities for collaboration in teaching and research, including student and faculty exchanges through Erasmus and CEEPUS programs.
Between July and August 2024, the ATHENA Christian Doppler Laboratory hosted four interns working on the following topics:
At the conclusion of their internships, they presented their work and results, receiving official certificates from the university. This collaboration was mutually beneficial for both the researchers at ATHENA and the interns. Their learning process was enhanced by the dedicated guidance they received, which included personalized mentorship, hands-on training, and continuous support. This comprehensive supervision ensured that they not only developed practical skills but also gained a deeper understanding of the research methodologies and technologies used in the video streaming field. We extend our gratitude to all three interns for their genuine interest, productive efforts, and valuable feedback on the laboratory.
Halime Lezi: “I had an awesome time during my four-week internship at ATHENA. My project was about image and video compression, and I learned a lot about how it works. I also got to use Python, which was both fun and challenging. The work environment at ATHENA was really supportive and interesting. My supervisor, Emanuele Artioli, was super helpful and always ready to answer my questions. He made sure I understood both the practical and theoretical parts of my work, which was really cool. It was also great to work with people from different countries. The team was friendly, and we got along well. The work-life balance was good, with a nice mix of work and relaxation. Overall, my time at ATHENA was very educational and enjoyable. The skills and knowledge I gained during this internship will be really useful for my future studies and career. I’m thankful for the opportunity and the support I received. I would highly recommend this internship to anyone looking for a rewarding and fulfilling experience. It’s a great place to learn, grow, and meet new people.”
Luka Kaiser: “I had an amazing four weeks at ATHENA. It was really nice meeting new colleagues and even making new friends. The project I worked on was exactly what interests me, and if I had any questions, my supervisor, Christian, was always there to help me out. So, thank you very much for that. Overall, this experience has been incredibly valuable, and I learned a lot and gained practical skills that I will definitely use in the future. My time here was both productive and enjoyable. I am grateful for the opportunity and would love to stay connected with everyone I’ve met. Thank you once again for everything.”
Julius van Dillen: “My four-week internship at ATHENA was incredible. I focused on improving image and video quality through Super-Resolution techniques. I had the opportunity to work with a variety of tools and technologies, including FFmpeg, Visual Studio Code, Python, several Super-Resolution architectures, and various video quality metrics. I was truly surprised by how much Super-Resolution enhances video and image quality. I really enjoyed working with these technologies and found the entire process fascinating. I am grateful to have had Daniele as my supervisor; his guidance and support made the experience both easier and more enjoyable. During my internship, I gained valuable insights into the research process and the fundamentals of Python programming.”
Authors: Mohammad Ghasempour (AAU, Austria), Hadi Amirpour (AAU, Austria), and Christian Timmerer (AAU, Austria)
Abstract: Video streaming has become an integral part of our digital lives, driving the need for efficient video delivery. With the growing demand for seamless video delivery, adaptive video streaming has emerged as a solution to support users with varying device capabilities and network conditions. Traditional adaptive streaming relies on a predetermined set of bitrate-resolution pairs, known as bitrate ladders, for encoding. However, this “one-size-fits-all” approach is suboptimal when dealing with diverse video content. Consequently, per-title encoding approaches dynamically select the bitrate ladder for each content. However, in an era when carbon dioxide emissions have become a paramount concern, it is crucial to consider energy consumption. Therefore, this paper addresses the pressing issue of increasing energy consumption in video streaming by introducing a novel approach, ESTR, which goes beyond traditional quality-centric resolution selection approaches. Instead, the ESTR considers both video quality and decoding energy consumption to construct an optimal bitrate ladder tailored to the unique characteristics of each video content. To accomplish this, ESTR encodes each video content using a range of spatial and temporal resolutions, each paired with specific bitrates. It then establishes a maximum acceptable quality drop threshold (τ), carefully selecting resolutions that not only preserve video quality above this threshold but also minimize decoding energy consumption. Our experimental results, at a fixed τ of 2 VMAF steps, demonstrate a 32.87% to 41.86% reduction in decoding energy demand for HEVC-encoded videos across various software decoder implementations and operating systems, with a maximum bitrate increase of 2.52%. Furthermore, on a hardware-accelerated client device, a 46.37% energy saving was achieved during video playback at the expense of a 2.52% bitrate increase. Remarkably, these gains in energy efficiency are achieved while maintaining consistent video quality.
From July 20-28, 2024, the 5th Edition of the Data Science International Summer School, managed by Bucharest Business School (BBS @ ASE) and in collaboration with GATE Institute at Sofia University “St. Kliment Ohridski” and the projects enRichMyData , Graph-Massivizer , UPCAST , INTEND , and InterTwino took place in Predeal, Romania. Radu participated as a speaker and mentor and also presented the Graph-Massivizer project.
Prestigious Speakers:
Dan Nicolae (University of Chicago, USA), Razvan Bunescu (University of North Carolina at Charlotte, USA), Anna Fensel Wageningen University & Research, the Netherlands), Radu Prodan (University of Klagenfurt, Austria), Ioan Toma (Onlim GmbH, Austria), Dumitru Roman (SINTEF / University of Oslo, Norway), Jože Rožanec (Qlector, Slovenia), Daniel Thilo Schroeder (SINTEF, Norway), Gabriel Terejanu, PhD (University of North Carolina at Charlotte, USA), Hui Song (SINTEF, Norway), Viktor Sowinski-Mydlarz (London Metropolitan University, UK and GATE Institute, Bulgaria), Roberto Avogadro (SINTEF, Norway), Nikolay Nikolov (SINTEF AS, Norway).
The EU has approved the DATAPACT project (Datapact: Compliance by Design of Data/AI Operations and Pipelines) application.The project has a total volume of 9,9 Mio. Euros and 19 partners, including ITEC (Radu Prodan).
DataPACT will develop novel tools and methodologies that enable efficient, compliant, ethical, and sustainable data/AI operations and pipelines. DataPACT will deliver a transformative approach where compliance, ethics, and environmental sustainability are not afterthoughts but foundational elements of data/AI operations and pipelines.
