Hadi
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Paper accepted – On The Impact of Viewing Distance on Perceived Video Quality

Title: On The Impact of Viewing Distance on Perceived Video Quality

Link: IEEE Visual Communications and Image Processing (VCIP 2021) 5-8 December 2021, Munich, Germany

Authors: Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Raimund Schatz (AIT Austrian Institute of Technology, Austria), Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt)

Abstract: Due to the growing importance of optimizing quality and efficiency of video streaming delivery, accurate assessment of user perceived video quality becomes increasingly relevant. However, due to the wide range of viewing distances encountered in real-world viewing settings, actually perceived video quality can vary significantly in everyday viewing situations. In this paper, we investigate and quantify the influence of viewing distance on perceived video quality.  A subjective experiment was conducted with full HD sequences at three different stationary viewing distances, with each video sequence being encoded at three different quality levels. Our study results confirm that the viewing distance has a significant influence on the quality assessment. In particular, they show that an increased viewing distance generally leads to an increased perceived video quality, especially at low media encoding quality levels. In this context, we also provide an estimation of potential bitrate savings that knowledge of actual viewing distance would enable in practice.
Since current objective video quality metrics do not systematically take into account viewing distance, we also analyze and quantify the influence of viewing distance on the correlation between objective and subjective metrics. Our results confirm the need for distance-aware objective metrics when accurate prediction of perceived video quality in real-world environments is required.

Hadi
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Paper accepted – Improving Per-title Encoding for HTTP Adaptive Streaming by Utilizing Video Super-resolution

Title: Improving Per-title Encoding for HTTP Adaptive Streaming by Utilizing Video Super-resolution

Link: IEEE Visual Communications and Image Processing (VCIP 2021) 5-8 December 2021, Munich, Germany

Authors: Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Hannaneh Barahouei Pasandi (Virginia Commonwealth University), Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt)

Abstract: In per-title encoding, to optimize a bitrate ladder over spatial resolution, each video segment is downscaled to a set of spatial resolutions and they are all encoded at a given set of bitrates. To find the highest quality resolution for each bitrate, the low-resolution encoded videos are upscaled to the original resolution, and a convex hull is formed based on the scaled qualities. Deep learning-based video super-resolution (VSR) approaches show a significant gain over traditional approaches and they are becoming more and more efficient over time.  This paper improves the per-title encoding over the upscaling methods by using deep neural network-based VSR algorithms as they show a significant gain over traditional approaches. Utilizing a VSR algorithm by improving the quality of low-resolution encodings can improve the convex hull. As a result, it will lead to an improved bitrate ladder. To avoid bandwidth wastage at perceptually lossless bitrates a maximum threshold for the quality is set and encodings beyond it are eliminated from the bitrate ladder. Similarly, a minimum threshold is set to avoid low-quality video delivery. The encodings between the maximum and minimum thresholds are selected based on one Just Noticeable Difference. Our experimental results show that the proposed per-title encoding results in a 24% bitrate reduction and 53% storage reduction compared to the state-of-the-art method.

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Invitation for MPEG AG 5 Workshop on Quality of Immersive Media: Assessment and Metrics

The Quality of Experience (QoE) is well-defined in QUALINET white papers [here, here], but its assessment and metrics are subject to research. The aim of this workshop on “Quality of Immersive Media: Assessment and Metrics” is to provide a forum for researchers and practitioners to discuss the latest findings in this field. The scope of this workshop is (i) to raise awareness about MPEG efforts in the context of quality of immersive visual media and (ii) invite experts (outside of MPEG) to present new techniques relevant to this workshop.

Quality assessments in the context of the MPEG standardization process typically serve two purposes: (1) to foster decision-making on the tool adoptions during the standardization process and (2) to validate the outcome of a standardization effort compared to an established anchor (i.e., for verification testing).

We kindly invite you to the first online MPEG AG 5 Workshop on Quality of Immersive Media: Assessment and Metrics as follows.

Logistics (online):

Program/Speakers:

15:00-15:10: Joel Jung & Christian Timmerer (AhG co-chairs): Welcome notice

15:10-15:30: Mathias Wien (AG 5 convenor): MPEG Visual Quality Assessment: Tasks and Perspectives
Abstract: The Advisory Group on MPEG Visual Quality Assessment (ISO/IEC JTC1 SC29/AG5) has been founded in 2020 with the goal to select and design subjective quality evaluation methodologies and objective quality metrics for the assessment of visual coding technologies in the context of the MPEG standardization work. In this talk, the current work items, as well as perspectives and first achievements of the group, are presented.

15:30-15:50: Aljosa Smolic: Perception and Quality of Immersive Media
Abstract: Interest in immersive media increased significantly over recent years. Besides applications in entertainment, culture, health, industry, etc., telepresence and remote collaboration gained importance due to the pandemic and climate crisis. Immersive media have the potential to increase social integration and to reduce greenhouse gas emissions. As a result, technologies along the whole pipeline from capture to display are maturing and applications are becoming available, creating business opportunities. One aspect of immersive technologies that is still relatively undeveloped is the understanding of perception and quality, including subjective and objective assessment. The interactive nature of immersive media poses new challenges to estimation of saliency or visual attention, and to the development of quality metrics. The V-SENSE lab of Trinity College Dublin addresses these questions in current research. This talk will highlight corresponding examples in 360 VR video, light fields, volumetric video and XR.

15:50-16:00: Break/Discussions

16:00-16:20: Jesús Gutiérrez: Quality assessment of immersive media: Recent activities within VQEG
Abstract: This presentation will provide an overview of the recent activities carried out on quality assessment of immersive media within the Video Quality Experts Group (VQEG), particularly within the Immersive Media Group (IMG). Among other efforts, outcomes will be presented from the cross-lab test (carried out by ten different labs) in order to assess and validate subjective evaluation methodologies for 360º videos, which was instrumental in the development of the ITU-T Recommendation P.919. Also, insights will be provided on the current plans on exploring the evaluation of the quality of experience of immersive communication systems, considering different technologies such as 360º video, point cloud, free-viewpoint video, etc.

16:20-16:40: Alexander Raake: <to-be-provided>

16:40-17:00: <to-be-provided>

17:00: Conclusions

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Paper accepted at IEEE GLOBECOM: Quality Optimization of Live Streaming Services over HTTP with Reinforcement Learning

Link: IEEE Global Communications Conference 2021

7-11 December 2021 // Madrid, Spain // Hybrid: In-Person and Virtual Conference Connecting Cultures around the Globe

Authors: F. Tashtarian*, R. Falanji‡, A. Bentaleb+, A. Erfanian*, P. S. Mashhadi§,
C. Timmerer*, H. Hellwagner*, R. Zimmermann+
Christian Doppler Laboratory ATHENA, Institute of Information Technology, Alpen-Adria-Universität Klagenfurt, Austria*
Department of Mathematical Science, Sharif University of Technology, Tehran, Iran‡
Department of Computer Science, School of Computing, National University of Singapore (NUS)+
Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden§

Abstract: Recent years have seen tremendous growth in HTTP adaptive live video traffic over the Internet. In the presence of highly dynamic network conditions and diverse request patterns, existing yet simple hand-crafted heuristic approaches for serving client requests at the network edge might incur a large overhead and significant increase in time complexity. Therefore, these approaches might fail in delivering acceptable Quality of Experience (QoE) to end users. To bridge this gap, we propose ROPL, a learning-based client request management solution at the edge that leverages the power of the recent breakthroughs in deep reinforcement learning, to serve requests of concurrent users joining various HTTP-based live video channels. ROPL is able to react quickly to any changes in the environment, performing accurate decisions to serve clients requests, which results in achieving satisfactory user QoE. We validate the efficiency of ROPL through trace-driven simulations and a real-world setup. Experimental results from real-world scenarios confirm that ROPL outperforms existing heuristic-based approaches in terms of QoE, with a factor up to 3.7×.

Index Terms—Network Edge; Request Serving; HTTP Live Streaming; Low Latency; QoE; Deep Reinforcement Learning.

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Paper accepted: End-to-end Quality of Experience Evaluation for HTTP Adaptive Streaming

Title: End-to-end Quality of Experience Evaluation for HTTP Adaptive Streaming

ACM MM’21: The 29th ACM International Conference on Multimedia

October  20-24, 2021,  Chengdu, China

Babak Taraghi (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)

Abstract: Exponential growth in multimedia streaming traffic over the Internet motivates the research and further investigation of the user’s perceived quality of such services. Enhancement of experienced quality by the users becomes more substantial when service providers compete on establishing superiority by gaining more subscribers or customers. Quality of Experience (QoE) enhancement would not be possible without an authentic and accurate assessment of the streaming sessions. HTTP Adaptive Streaming (HAS) is today’s prevailing technique to deliver the highest possible audio and video content quality to the users. An end-to-end evaluation of QoE in HAS covers the precise measurement of the metrics that affect the perceived quality, eg. startup delay, stall events, and delivered media quality. Mentioned metrics improvements could limit the service’s scalability, which is an important factor in real-world scenarios. In this study, we will investigate the stated metrics, best practices and evaluations methods, and available techniques with an aim to (i) design and develop practical and scalable measurement tools and prototypes, (ii) provide a better understanding of current technologies and techniques (eg. Adaptive Bitrate algorithms), (iii) conduct in-depth research on the significant metrics in a way that improvements of QoE with scalability in mind would be feasible, and finally, (iv) provide a comprehensive QoE model which outperforms state-of-the-art models.

Keywords: HTTP Adaptive Streaming; Quality of Experience; Subjective Evaluation; Objective Evaluation; Adaptive Bitrate; QoE model.

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Paper accepted: CTU Depth Decision Algorithms for HEVC: A Survey

Title: CTU Depth Decision Algorithms for HEVC: A Survey

Link: Signal Processing: Image Communication

[PDF]

Ekrem Çetinkaya* (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Hadi Amirpour*, (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Mohammad Ghanbari (Christian Doppler Laboratory ATHENA, University of Essex),  and Christian Timmerer (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)

*These authors contributed equally to this work.

Abstract: High Efficiency Video Coding (HEVC) surpasses its predecessors in encoding efficiency by introducing new coding tools at the cost of an increased encoding time-complexity. The Coding Tree Unit (CTU) is the main building block used in HEVC. In the HEVC standard, frames are divided into CTUs with the predetermined size of up to 64 × 64 pixels. Each CTU is then divided recursively into a number of equally sized square areas, known as Coding Units (CUs). Although this diversity of frame partitioning increases encoding efficiency, it also causes an increase in the time complexity due to the increased number of ways to find the optimal partitioning. To address this complexity, numerous algorithms have been proposed to eliminate unnecessary searches during partitioning CTUs by exploiting the correlation in the video. In this paper, existing CTU depth decision algorithms for HEVC are surveyed. These algorithms are categorized into two groups, namely statistics and machine learning approaches. Statistics approaches are further subdivided into neighboring and inherent approaches. Neighboring approaches exploit the similarity between adjacent CTUs to limit the depth range of the current CTU, while inherent approaches use only the available information within the current CTU. Machine learning approaches try to extract and exploit similarities implicitly. Traditional methods like support vector machines or random forests use manually selected features, while recently proposed deep learning methods extract features during training. Finally, this paper discusses extending these methods to more recent video coding formats such as Versatile Video Coding (VVC) and AOMedia Video 1 (AV1).

Keywords: HEVC, Coding Tree Unit, Complexity, CTU Partitioning, Statistics, Machine Learning

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Drone researcher Michał Barcis and team win the Drone Bot Contest at Deep Drone Challenge 2021

Agata and Michał Barciś and their fellow researcher from RTB House in Poland, Michał Jagielski, competed in the Drone Bot Contest at the Deep Drone Challenge in Ingolstadt, Germany on Saturday 7 August 2021.

The competition is organised by start-up incubator brigkAIR and Europe’s largest aircraft manufacturer Airbus. The three young scientists were delighted to receive a prize of 25,000 Euros.

Read more about it here.

Vignesh V Menon
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Vignesh V Menon to give a talk at the Research@Lunch Special

Vignesh V Menon is invited to talk on “Video Coding for HTTP Adaptive Streaming” on the Research@Lunch is a research webinar series by Humanitarian Technology (HuT) Labs, Amrita Vishwa Vidyapeetham University, India, exclusively for Ph.D. Scholars, UG, and PG Researchers in India.  This talk will introduce the basics of video codecs and highlight the scope of HAS-related research on video encoding.

Time: August 14, 10.00AM-10.30AM (CEST) or 1.30PM- 2.00PM (IST)

Registration form can be found here.

 

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ATHENA Papers Accepted at ACM MMSys’21 Doctoral Symposium

ACM Multimedia Systems Conference (MMSys) 2021 | Doctoral Symposium

September 28 – October 01, 2021 | Istanbul, Turkey

Conference Website

Read more

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Paper published at Frontiers in Robotics and AI: Information Distribution in Multi-Robot Systems: Generic, Utility-Aware Optimization Middleware

Authors: M. Barciś, A. Barciś, N. Tsiogkas, H. Hellwagner.

Title: Information Distribution in Multi-Robot Systems: Generic, Utility-Aware Optimization Middleware.

Frontiers in Robotics and AI 8:685105, July 2021.

This work addresses the problem of what information is worth sending in a multi-robot system under generic constraints, e.g., limited throughput or energy. Our decision method is based on Monte Carlo Tree Search. It is designed as a transparent middleware that can be integrated into existing systems to optimize communication among robots. Furthermore, we introduce techniques to reduce the decision space of this problem to further improve the performance. We evaluate our approach using a simulation study and demonstrate its feasibility in a real-world environment by realizing a proof of concept in ROS 2 on mobile robots.

Published paper