At the PCS 2024 (Picture Coding Symposium), held in Taichung, Taiwan from June 12-14, Hadi Amirpour received the Best Paper Award for the paper “Beyond Curves and Thresholds – Introducing Uncertainty Estimation To Satisfied User Ratios for Compressed Video” written together with Jingwen Zhu, Raimund Schatz, Patrick Le Callet and Christian Timmerer. Congratulations!
To celebrate the 40th birthday of a video game classic, Lukas Lorber from Kleine Zeitung interviewed Felix Schniz about Tetris. The interview touches upon the Cold War history of the video game, the psychology behind the ‘Tetris Effect’, and various annotations by genre expert Felix Schniz about the secret behind the game’s ongoing success.
You can read the full interview here: https://www.kleinezeitung.at/wirtschaft/gaming/18530006/40-jahre-tetris-aus-dem-kalten-krieg-in-die-unsterblichkeit.
Authors: Yiying Wei (AAU, Austria), Hadi Amirpour (AAU, Austria), Ahmed Telili (INSA Rennes, France), Wassim Hamidouche (INSA Rennes, France), Guo Lu (Shanghai Jiao Tong University, China) and Christian Timmerer (AAU, Austria)
Venue: European Signal Processing Conference (EUSIPCO)
Abstract: Content-aware deep neural networks (DNNs) are trending in Internet video delivery. They enhance quality within bandwidth limits by transmitting videos as low-resolution (LR) bitstreams with overfitted super-resolution (SR) model streams to reconstruct high-resolution (HR) video on the decoder end. However, these methods underutilize spatial and temporal redundancy, compromising compression efficiency. In response, our proposed video compression framework introduces spatial-temporal video super-resolution (STVSR), which encodes videos into low spatial-temporal resolution (LSTR) content and a model stream, leveraging the combined spatial and temporal reconstruction capabilities of DNNs. Compared to the state-of-the-art approaches that consider only spatial SR, our approach achieves bitrate savings of 18.71% and 17.04% while maintaining the same PSNR and VMAF, respectively.
Authors: Mohammad Ghasempour (AAU, Austria), Yiying Wei (AAU, Austria), Hadi Amirpour (AAU, Austria), and Christian Timmerer (AAU, Austria)
Venue: European Signal Processing Conference (EUSIPCO)
Abstract: Video coding relies heavily on reducing spatial and temporal redundancy to enable efficient transmission. To tackle the temporal redundancy, each video frame is predicted from the previously encoded frames, known as reference frames. The quality of this prediction is highly dependent on the quality of the reference frames. Recent advancements in machine learning are motivating the exploration of frame synthesis to generate high-quality reference frames. However, the efficacy of such models depends on training with content similar to that encountered during usage, which is challenging due to the diverse nature of video data. This paper introduces a content-aware reference frame synthesis to enhance inter-prediction efficiency. Unlike conventional approaches that rely on pre-trained models, our proposed framework optimizes a deep learning model for each content by fine-tuning only the last layer of the model, requiring the transmission of only a few kilobytes of additional information to the decoder. Experimental results show that the proposed framework yields significant bitrate savings of 12.76%, outperforming its counterpart in the pre-trained framework, which only achieves 5.13% savings in bitrate.
Authors: Zoha Azimi, Amritha Premkumar, Reza Farahani, Vignesh V Menon, Christian Timmerer, Radu Prodan
Venue: 32nd European Signal Processing Conference (EUSIPCO’24)
Abstract: Traditional per-title encoding approaches aim to maximize perceptual video quality by optimizing resolutions for each bitrate ladder representation. However, ensuring acceptable decoding times in video streaming, especially with the increased runtime complexity of modern codecs like Versatile Video Coding (VVC) compared to predecessors such as High Efficiency Video Coding (HEVC), is essential, as it leads to diminished buffering time, decreased energy consumption, and an improved Quality of Experience (QoE). This paper introduces a decoding complexity-sensitive bitrate ladder estimation scheme designed to optimize adaptive VVC streaming experiences. We design a customized bitrate ladder for the device configuration, ensuring that the
decoding time remains below the threshold to mitigate adverse QoE issues such as rebuffering and to reduce energy consumption. The proposed scheme utilizes an eXtended PSNR (XPSNR)-optimized resolution prediction for each target bitrate, ensuring
the highest possible perceptual quality within the constraints of device resolution and decoding time. Furthermore, it employs XGBoost-based models for predicting XPSNR, QP, and decoding time, utilizing the Inter-4K video dataset for training. The
experimental results indicate that our approach achieves an average 28.39 % reduction in decoding time using the VVC Test Model (VTM). Additionally, it achieves bitrate savings of 3.7 % and 1.84 % to maintain almost the same PSNR and XPSNR,
respectively, for a display resolution constraint of 2160p and a decoding time constraint of 32 s.
The Second Workshop on Serverless, Extreme-Scale, and Sustainable Graph Processing Systems (GraphSys ’24) took place in South Kensington, London, co-located with the 15th ACM/SPEC International Conference on Performance Engineering.
Reza Farahani gave a talk entitled “Serverless Workflow Management Systems on the Computing Continuum”
Authors: Reza Farahani (AAU, Klagenfurt, Austria), Frank Loh (University of Würzburg, Germany), Dumitru Roman (Sintef, Oslo, Norway), Radu Prodan (AAU, Klagenfurt, Austria)
Abstract: The growing desire among application providers for a cost model based on pay-per-use, combined with the need for a seamlessly integrated platform to manage the complex workflows of their applications, has spurred the emergence of a promising computing paradigm known as serverless computing. Although serverless computing was initially considered for cloud environments, it has recently been extended to other layers of the computing continuum, i.e., edge and fog. This extension emphasizes that the proximity of computational resources to data sources can further reduce costs and improve performance and energy efficiency. However, orchestrating the computing continuum in complex application workflows, including a set of serverless functions, introduces new challenges. This paper investigates the opportunities and challenges introduced by serverless computing for workflow management systems (WMS) on the computing continuum. In addition, the paper provides a taxonomy of state-of-the-art WMSs and reviews their capabilities.
Furthermore Reza Farahani and the backend Graph-Massivizer team met to discuss Graph-Massivizer toolkit integration plan.
Dragi Kimovski co-chaired the 7th Workshop on Hot Topics in Cloud Computing Performance (HotCloudPerf 2024) workshop within the International Conference on Performance Engineering (ICPE). During the workshop, he presented a paper titled “Hypergraphs: Facilitating High-Order Modeling of the Computing Continuum.” This event, held at Imperial College London on May 11, 2024, focused on various aspects of cloud computing performance, including elasticity, performance isolation, and dependability.
On May 8th, 2024, Mathias, Tom, and a group of helpers organized the first internal generative AI mini hackathon. More than ten people participated and tried their hand at using various forms of generative AI – such as text, image, sound, and 3D model generation. After 8 hours of coding and testing, the common goal of creating an engine to generate new Pokemon-like creatures started to take shape and achieved some presentable results! Much was learned about how much is reachable in such a short period, and insights into many potential uses of generative AI were gained. The event also fostered contact between ITEC and Athena Lab employees, ISYS, and NES! Using what was learned, a locally hosted LLM (akin to ChatGPT) for ITEC will be presented soon and possibly extended for university-wide use later. Thank you to everyone who attended; hopefully, similar events can be hosted again in the future!
On the weekend of April 27-28th, HaruCon, Carinthea’s youth pop culture convention, took place (https://www.harucon.at/) in Klagenfurt. Felix, Tom, Claudia, Sebastian, and many Game Studies and Engineering students were present and represented GSE, TEWI, and the university. Tom and Sebastian held a workshop on how to enter the video game industry, while Felix held one for an introduction to video game analysis. The convention was an enormous success, with more than 2000 visitors over two days. Flyers, buttons, and stickers were handed out to everyone so that awareness of the university as part of Klagenfurt’s youth culture could continue to grow. How to study there, especially video games and many other burning questions were answered by our brave helpers more than a hundred times during the convention.
Authors: Zoha Azimi, Reza Farahani, Vignesh V Menon, Christian Timmerer, Radu Prodan
Venue: 16th International Conference on Quality of Multimedia Experience (QoMEX’24)
Abstract: As video streaming dominates Internet traffic, users constantly seek a better Quality of Experience (QoE), often resulting in increased energy consumption and a higher carbon footprint. The increasing focus on sustainability underscores the
critical need to balance energy consumption and QoE in video streaming. This paper proposes a modular architecture that refines video encoding parameters by assessing video complexity and encoding settings for the prediction of energy consumption and video quality (based on Video Multimethod Assessment Fusion (VMAF)) using lightweight XGBoost models trained on the multi-dimensional video compression dataset (MVCD). We apply Explainable AI (XAI) techniques to identify the critical encoding parameters that influence the energy consumption and video quality prediction models and then tune them using a weighting strategy between energy consumption and video quality. The experimental results confirm that applying a suitable weighting factor to energy consumption in the x265 encoder results in a 46 % decrease in energy consumption, with a 4-point drop in VMAF, staying below the Just Noticeable Difference (JND) threshold.