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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

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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.

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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

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Paper accepted at IEEE ACCESS: LwTE: Light-weight Transcoding at the Edge

Authors: Alireza Erfanian* (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Hadi Amirpour*, (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Farzad Tashtarian (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt),  Christian Timmerer (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Hermann Hellwagner (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)

*These authors contributed equally to this work.

Link: IEEE Access

Abstract: Due to the growing demand for video streaming services, providers have to deal with increasing resourcerequirements for increasingly heterogeneous environments. To mitigate this problem, many works have beenproposed which aim to (i) improve cloud/edge caching efficiency, (ii) use computation power available in thecloud/edge for on-the-fly transcoding, and (iii) optimize the trade-off among various cost parameters,e.g.,storage, computation, and bandwidth. In this paper, we proposeLwTE, a novelLight-weightTranscodingapproach at theEdge, in the context of HTTP Adaptive Streaming (HAS). During the encoding processof a video segment at the origin side, computationally intense search processes are going on. The mainidea ofLwTEis to store the optimal results of these search processes as metadata for each video bitrateand reuse them at the edge servers to reduce the required time and computational resources for on-the-fly transcoding.LwTEenables us to store only the highest bitrate plus corresponding metadata (of verysmall size) for unpopular video segments/bitrates. In this way, in addition to the significant reduction inbandwidth and storage consumption, the required time for on-the-fly transcoding of a requested segment isremarkably decreased by utilizing its corresponding metadata; unnecessary search processes are avoided.Popular video segments/bitrates are being stored. We investigate our approach for Video-on-Demand (VoD)streaming services by optimizing storage and computation (transcoding) costs at the edge servers and thencompare it to conventional methods (store all bitrates, partial transcoding). The results indicate that ourapproach reduces the transcoding time by at least 80% and decreases the aforementioned costs by 12% to70% compared to the state-of-the-art approaches.

Keywords: Video streaming, transcoding, video on demand, edge computing.

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Paper accepted at IEEE 23rd International Workshop on Multimedia Signal Processing – WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile Devices

Title: WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile Devices

IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP)

October 06-08, Tampere, Finland

Authors: 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: Recently, mobile devices have become paramount in online video streaming. Adaptive bitrate (ABR) algorithms of players responsible for selecting the quality of the videos face critical challenges in providing a high Quality of Experience (QoE) for end users. One open issue is how to ensure the optimal experience for heterogeneous devices in the context of extreme variation of mobile broadband networks. Additionally, end users may have different priorities on video quality and data usage (i.e., the amount of data downloaded to the devices through the mobile networks). A generic mechanism for players that enables specification of various policies to meet end users’ needs is still missing. In this paper, we propose a weighted sum model, namely WISH, that yields high QoE of the video and allows end users to express their preferences among different parameters (i.e., data usage, stall events, and video quality) of video streaming. WISH has been implemented into ExoPlayer, a popular player used in many mobile applications. The experimental results show that WISH improves the QoE by up to 17.6% while saving 36.4% of data usage compared to state-of-the-art ABR algorithms and provides dynamic adaptation to end users’ requirements.

Keywords: ABR Algorithms, HTTP Adaptive Streaming, ITU-T P.1203, WISH

Vignesh V Menon
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Accepted Paper at IEEE 23rd International Workshop on Multimedia Signal Processing – INCEPT: INTRA CU Depth Prediction for HEVC

Title: INCEPT: INTRA CU Depth Prediction for HEVC

IEEE 23rd International Workshop on Multimedia Signal Processing

October 06–08, 2021, Tampere, Finland

Authors: Vignesh V Menon (Alpen-Adria-Universitat Klagenfurt); Hadi Amirpour (Alpen-Adria-Universität Klagenfurt); Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria); Mohammad Ghanbari (University of Essex, UK).

Abstract: High Efficiency Video Coding (HEVC) improves the encoding efficiency by utilizing sophisticated tools such as flexible Coding Tree Unit (CTU) partitioning. The Coding Unit (CU) can be split recursively into four equally sized CUs ranging from 64×64 to 8×8 pixels. At each depth level (or CU size), intra prediction via exhaustive mode search was exploited in HEVC to improve the encoding efficiency and result in a very high encoding time complexity. This paper proposes an Intra CU Depth Prediction (INCEPT) algorithm, which limits Rate-Distortion Optimization (RDO) for each CTU in HEVC by utilizing the spatial correlation with the neighboring CTUs, which is computed using a DCT energy-based feature. Thus, INCEPT reduces the number of candidate CU sizes required to be considered for each CTU in HEVC intra coding. Experimental results show that the INCEPT algorithm achieves a better trade-off between the encoding efficiency and encoding time saving (i.e., BDR/∆T) than the benchmark algorithms. While BDR/∆T is 12.35% and 9.03% for the benchmark algorithms, it is 5.49% for the proposed algorithm. As a result, INCEPT achieves a 23.34% reduction in encoding time on average while incurring only a 1.67% increase in bit rate than the original coding in the x265 HEVC open-source encoder.

Keywords: HEVC, Intra coding, CTU, CU, depth decision

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Successful completion and demo of the Karl Popper Kolleg (KPK) Networked Autonomous Aerial Vehicles (NAV)

Robotics research in Klagenfurt enjoys international success

With a total of 9 contributions at this year’s ICRA, one of the flagship conferences in the field of robotics, the University of Klagenfurt has joined the league of the world’s most important robotics hubs. Among the contributors are the young researchers from the Karl Popper Doktorats- und Wissenschaftskolleg “Networked Autonomous Aerial Vehicles (NAV)”, which is currently celebrating its conclusion with a drone flight demonstration in Klagenfurt. Read more at the University Klagenfurt blog and here.

 

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KWF/EFRE project “ONTIS” accepted

The project “ONTIS” (Ontology-based Interoperability of Systems) has been accepted in the EFRE call of KWF (Kärntner Wirtschaftsförderungs Fonds).

The ONTIS project targets the development of methodologies for automatically establishing interoperability between information systems through the combination of ontological expert knowledge and machine learning-based models. With the specific goal of improving the error-prone manual integration of ontological knowledge, ONTIS focuses on applying deep neural networks for processing natural language and visual concepts for automatic semantic annotation.

Project duration: 18 months

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Paper accepted: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming

Conference info: IEEE LCN

Authors: Jesús Aguilar Armijo (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt) and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)

Abstract: Mobile networks equipped with edge computing nodes enable access to information that can be leveraged to assist client-based adaptive bitrate (ABR) algorithms in making better adaptation decisions to improve both Quality of Experience (QoE) and fairness. For this purpose, we propose a novel on-the-fly edge mechanism, named EADAS (Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming), located at the edge node that assists and improves the ABR decisions on-the-fly. EADAS proposes (i) an edge ABR algorithm to improve QoE and fairness for clients and (ii) a segment prefetching scheme. The results show a QoE increase of 4.6%, 23.5%, and 24.4% and a fairness increase of 11%, 3.4%, and 5.8% when using a buffer-based, a throughput-based, and a hybrid ABR algorithm, respectively, at the client compared with client-based algorithms without EADAS. Moreover, QoE and fairness among clients can be prioritized using parameters of the EADAS algorithm according to service providers’ requirements.

Keywords: Dynamic Adaptive Streaming over HTTP (DASH), Edge Computing, Network-Assisted Video Streaming, Quality of Experience (QoE).