Prof. Radu Prodan

Paper accepted in IEEE Computer Magazine: Big Data Pipelines on the Computing Continuum: Tapping the Dark Data

Title: Big Data Pipelines on the Computing Continuum: Tapping the Dark Data

 

Authors: Dumitru Roman, Radu Prodan, Nikolay Nikolov, Ahmet Soylu, Mihhail Matskin, Andrea Marrella, Dragi Kimovski, Brian Elvesæter, Anthony Simonet-Boulogne, Giannis Ledakis, Hui Song, Francesco Leotta, Evgeny Kharlamov

 

Abstract: Big Data pipelines are essential for leveraging Dark Data, i.e., data collected but not used and turned into value. However, tapping their potential requires going beyond existing approaches and frameworks for Big Data processing. The Computing Continuum enables new opportunities for managing Big Data pipelines concerning efficient management of heterogeneous and untrustworthy resources. This article discusses the Big Data pipelines lifecycle on the Computing Continuum, its associated challenges and outlines a future research agenda in this area.

Vignesh V Menon
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Paper accepted: Live-PSTR: Live Per-title Encoding for Ultra HD Adaptive Streaming

2022 NAB Broadcast Engineering and Information Technology (BEIT) Conference

April 24-26, 2022 | Las Vegas, US

Conference Website

Vignesh V Menon (Alpen-Adria-Universität Klagenfurt),  Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Christian Feldmann (Bitmovin, Klagenfurt),
Adithyan Ilangovan (Bitmovin, Klagenfurt), Martin Smole (Bitmovin, Klagenfurt), Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt).

Abstract:

Current per-title encoding schemes encode the same video content at various bitrates and spatial resolutions to find optimal bitrate-resolution pairs (known as bitrate ladder) for each video content in Video on Demand (VoD) applications. But in live streaming applications, a fixed bitrate ladder is used for simplicity and efficiency to avoid the additional latency to find the optimized bitrate-resolution pairs for every video content. However, an optimized bitrate ladder may result in (i) decreased storage or network resources or/and (ii) increased Quality of Experience (QoE). In this paper, a fast and efficient per-title encoding scheme (Live-PSTR) is proposed tailor-made for live Ultra High Definition (UHD) High Framerate (HFR) streaming. It includes a pre-processing step in which Discrete Cosine Transform (DCT)-energy-based low-complexity spatial and temporal features are used to determine the complexity of each video segment, based on which the optimized encoding resolution and framerate for streaming at every target bitrate is determined. Experimental results show that, on average, Live-PSTR yields bitrate savings of 9.46% and 11.99% to maintain the same PSNR and VMAF scores, respectively compared to the HTTP Live Streaming (HLS) bitrate ladder.

Architecture of Live-PSTR

VCA v1.0 released on Valentine’s day

As a Valentine’s day gift to video coding enthusiasts across the globe, we release Video Complexity Analyzer (VCA) version 1.0 open-source software on Feb 14, 2022. The primary objective of VCA is to become the best spatial and temporal complexity predictor for every frame/ video segment/ video which aids in predicting encoding parameters for applications like scene-cut detection and online per-title encoding. VCA leverages x86 SIMD and multi-threading optimizations for effective performance. While VCA is primarily designed as a video complexity analyzer library, a command-line executable is provided to facilitate testing and development. We expect VCA to be utilized in many leading video encoding solutions in the coming years.

VCA is available as an open-source library, published under the GPLv3 license. For more details, please visit the software online documentation here. The source code can be found here.

Heatmap of spatial complexity (E)

Heatmap of temporal complexity (h)

 

 

 

 

 

 

 

 

 

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Paper accepted: Take the Red Pill for H3 and See How Deep the Rabbit Hole Goes

ACM Mile-High video 2022 (MHV)

March 01-03, 2022 | Denver, CO, USA

Conference Website

Authors: Minh Nguyen (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt, Austria), Christian Timmerer (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt, Austria), Stefan Pham (Fraunhofer FOKUS, Germany), Daniel Silhavy (Fraunhofer FOKUS, Germany), Ali C. Begen (Ozyegin University, Turkey)

Abstract: With the introduction of HTTP/3 (H3) and QUIC at its core, there is an expectation of significant improvements in Web-based secure object delivery. As HTTP is a central protocol to the current adaptive streaming methods in all major over-the-top (OTT) services, an important question is what H3 will bring to the table for such services. To answer this question, we present the new features of H3 and QUIC, and compare them to those of H/1.1/2 and TCP. We also share the latest research findings in this domain.

Keywords: HTTP adaptive streaming, QUIC, CDN, ABR, OTT, DASH, HLS.

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MPEG DASH video streaming technology co-developed in Klagenfurt wins Technology & Engineering Emmy® Award

The Emmy® Awards do not only honour the work of actors and directors, but they also recognise technologies that are steadily improving the viewing experience for consumers. This year, the winners include the MPEG DASH Standard. Christian Timmerer (Department of Information Technology) played a leading role in its development. Read more about it here.

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Paper accepted: Super-resolution Based Bitrate Adaptation for HTTP Adaptive Streaming for Mobile Devices

ACM Mile-High video 2022 (MHV)

March 01-03, 2022 | Denver, CO, USA

Conference Website

Minh Nguyen (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Ekrem Çetinkaya (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Hermann Hellwagner (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), and Christian Timmerer (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)

Abstract: The advancement of mobile hardware in recent years made it possible to apply deep neural network (DNN) based approaches on mobile devices. This paper introduces a lightweight super-resolution (SR) network, namely SR-ABR Net, deployed at mobile devices to upgrade low-resolution/low-quality videos and a novel adaptive bitrate (ABR) algorithm, namely WISH-SR, that leverages SR networks at the client to improve the video quality depending on the client’s context. WISH-SR takes into account mobile device properties, video characteristics, and user preferences. Experimental results show that the proposed SR-ABR Net can improve the video quality compared to traditional SR approaches while running in real-time. Moreover, the proposed WISH-SR can significantly boost the visual quality of the delivered content while reducing both bandwidth consumption and the number of stalling events.

Keywords: Super-resolution, Deep Neural Networks, Mobile Devices, ABR

Prof. Radu Prodan
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Exploit 5G potential: Research team builds “Carinthian Fog”

In Carinthia, researchers find an open test laboratory in the 5G Playground Carinthia, where the possibilities of the new mobile phone technology can be explored. The problem is: 5G enables the fast transmission of large amounts of data, but these also have to be processed. Read the whole interview of Univ.-Prof. DI Dr. Radu Prodan in the latest University Klagenfurt news.

Hadi
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Hadi Amirpour has successfully defended his PhD thesis

On Tuesday the 25th of January 2022, Hadi Amirpour successfully defended his Ph.D. thesis under supervision of Assoc.-Prof. DI Dr. Christian Timmerer and Assoc.-Prof. Dr. Klaus Schöffmann. The defense was chaired by Assoc.-Prof. DI Dr. Mathias Lux and the examiners were Emeritus Prof. Dr. Mohammad Ghanbari (University of Essex, UK) and Univ.-Prof. DI Dr. Hermann Hellwagner (University of Klagenfurt).

We are pleased to congratulate Dr. Hadi Amirpour on passing his Ph.D. exam!

FF4EuroHPC Project “CardioHPC” accepted

The project “CardioHPC” (CardioHPC Improving DL-based Arrhythmia Classification Algorithm and Simulation of Real-Time Heart Monitoring of Thousands of Patients) has been accepted in the “First call for FF4EuroHPC application experiments” (funded under the European Community’s Horizon 2020 Programme). Prof. Prodan will take over the project management in Klagenfurt.

The goal is to conduct an experiment to improve our DL-based arrhythmia classification algorithm and conduct a large-scale demonstration experiment to simulate a monitoring center for automated monitoring and alerting for 10K patients through HPC, focusing on quality and identifying HPC as a key tool for innovation.

Project Partners: The University of Stuttgart, Innovation Dooel, University in Skopje

Project duration: 15 months

Vignesh V Menon

Paper accepted – OPTE: Online Per-title Encoding for Live Video Streaming

2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

May 22-27, 2022 | Singapore

Conference Website

Vignesh V Menon (Alpen-Adria-Universität Klagenfurt),  Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt).

Abstract:

Current per-title encoding schemes encode the same video content at various bitrates and spatial resolutions to find an optimal bitrate ladder for each video content in Video on Demand (VoD) applications. However, in live streaming applications, a fixed resolution-bitrate ladder is used to avoid the additional encoding time complexity to find optimum resolution-bitrate pairs for every video content. This paper introduces an online per-title encoding scheme (OPTE) for live video streaming applications. In this scheme, each target bitrate’s optimal resolution is predicted from any pre-defined set of resolutions using Discrete Cosine Transform(DCT)-energy-based low-complexity spatial and temporal features for each video segment. Experimental results show that, on average, OPTE yields bitrate savings of 20.45% and 28.45% to maintain the same PSNR and VMAF, respectively, compared to a fixed bitrate ladder scheme (as adopted in current live streaming deployments) without any noticeable additional latency in streaming.

Keywords:

Per-title encoding, live streaming, bitrate ladder, convex-hull prediction