[551] | Vladislav Kashansky, Dragi Kimovski, Radu Prodan, Prateek Agrawal, Fabrizio Marozzo, Gabriel Iuhasz, Marek Marozzo, Javier Garcia-Blas, M3AT: Monitoring Agents Assignment Model for Data-Intensive Applications, In 2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), IEEE, pp. 72-79, 2020.
[bib][url] [doi] [abstract]
Abstract: Nowadays, massive amounts of data are acquired, transferred, and analyzed nearly in real-time by utilizing a large number of computing and storage elements interconnected through high-speed communication networks. However, one issue that still requires research effort is to enable efficient monitoring of applications and infrastructures of such complex systems. In this paper, we introduce a Integer Linear Programming (ILP) model called M3AT for optimised assignment of monitoring agents and aggregators on large-scale computing systems. We identified a set of requirements from three representative data-intensive applications and exploited them to define the model’s input parameters. We evaluated the scalability of M3AT using the Constraint Integer Programing (SCIP) solver with default configuration based on synthetic data sets. Preliminary results show that the model provides optimal assignments for systems composed of up to 200 monitoring agents while keeping the number of aggregators constant and demonstrates variable sensitivity with respect to the scale of monitoring data aggregators and limitation policies imposed.
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[550] | Jeroen van der Hooft, Maria Torres Vega, Christian Timmerer, Ali C. Begen, Filip De Turck, Raimund Schatz, Objective and Subjective QoE Evaluation for Adaptive Point Cloud Streaming, In 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2020.
[bib][url] [doi] [abstract]
Abstract: Volumetric media has the potential to provide the six degrees of freedom (6DoF) required by truly immersive media. However, achieving 6DoF requires ultra-high bandwidth transmissions, which real-world wide area networks cannot provide economically. Therefore, recent efforts have started to target efficient delivery of volumetric media, using a combination of compression and adaptive streaming techniques. It remains, however, unclear how the effects of such techniques on the user perceived quality can be accurately evaluated. In this paper, we present the results of an extensive objective and subjective quality of experience (QoE) evaluation of volumetric 6DoF streaming. We use PCC-DASH, a standards-compliant means for HTTP adaptive streaming of scenes comprising multiple dynamic point cloud objects. By means of a thorough analysis we investigate the perceived quality impact of the available bandwidth, rate adaptation algorithm, viewport prediction strategy and user’s motion within the scene. We determine which of these aspects has more impact on the user’s QoE, and to what extent subjective and objective assessments are aligned.
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[549] | Cathal Gurrin, Tu-Khiem Le, Van-Tu Ninh, Duc-Tien Dang-Nguyen, Björn Thor Jonsson, Jakub Loko, Wolfgang Hürst, Minh-Triet Tran, Klaus Schöffmann, Introduction to the Third Annual Lifelog Search Challenge (LSC' 20), In Proceedings of the 2020 International Conference on Multimedia Retrieval, ACM, pp. 584-585, 2020.
[bib][url] [doi] [abstract]
Abstract: The Lifelog Search Challenge (LSC) is an annual comparative benchmarking activity for comparing approaches to interactive retrieval from multi-modal lifelogs. LSC'20, the third such challenge, attracts fourteen participants with their interactive lifelog retrieval systems. These systems are comparatively evaluated in front of a live-audience at the LSC workshop at ACM ICMR'20 in Dublin, Ireland. This overview motivates the challenge, presents the dataset and system configuration used in the challenge, and briefly presents the participating teams.
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[548] | Negin Ghamsarian, Mario Taschwer, Klaus Schoeffmann, Deblurring Cataract Surgery Videos Using a Multi-Scale Deconvolutional Neural Network, In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), IEEE, pp. 872-876, 2020.
[bib][url] [doi] [abstract]
Abstract: A common quality impairment observed in surgery videos is blur, caused by object motion or a defocused camera. Degraded image quality hampers the progress of machine-learning-based approaches in learning and recognizing semantic information in surgical video frames like instruments, phases, and surgical actions. This problem can be mitigated by automatically deblurring video frames as a preprocessing method for any subsequent video analysis task. In this paper, we propose and evaluate a multi-scale deconvolutional neural network to deblur cataract surgery videos. Experimental results confirm the effectiveness of the proposed approach in terms of the visual quality of frames as well as PSNR improvement.
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[547] | Negin Ghamsarian, Enabling Relevance-Based Exploration of Cataract Videos, In Proceedings of the 2020 International Conference on Multimedia Retrieval, ACM, pp. 378-382, 2020.
[bib][url] [doi] [abstract]
Abstract: Training new surgeons as one of the major duties of experienced expert surgeons demands a considerable supervisory investment of them. To expedite the training process and subsequently reduce the extra workload on their tight schedule, surgeons are seeking a surgical video retrieval system. Automatic workflow analysis approaches can optimize the training procedure by indexing the surgical video segments to be used for online video exploration. The aim of the doctoral project described in this paper is to provide the basis for a cataract video exploration system, that is able to (i) automatically analyze and extract the relevant segments of videos from cataract surgery, and (ii) provide interactive exploration means for browsing archives of cataract surgery videos. In particular, we apply deep-learning-based classification and segmentation approaches to cataract surgery videos to enable automatic phase and action recognition and similarity detection.
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[546] | Negin Ghamsarian, Hadi Amirpourazarian, Christian Timmerer, Mario Taschwer, Klaus Schöffmann, Relevance-Based Compression of Cataract Surgery Videos Using Convolutional Neural Networks, In Proceedings of the 28th ACM International Conference on Multimedia, ACM, pp. 3577-3585, 2020.
[bib][url] [doi] [abstract]
Abstract: Recorded cataract surgery videos play a prominent role in training and investigating the surgery, and enhancing the surgical outcomes. Due to storage limitations in hospitals, however, the recorded cataract surgeries are deleted after a short time and this precious source of information cannot be fully utilized. Lowering the quality to reduce the required storage space is not advisable since the degraded visual quality results in the loss of relevant information that limits the usage of these videos. To address this problem, we propose a relevance-based compression technique consisting of two modules: (i) relevance detection, which uses neural networks for semantic segmentation and classification of the videos to detect relevant spatio-temporal information, and (ii) content-adaptive compression, which restricts the amount of distortion applied to the relevant content while allocating less bitrate to irrelevant content. The proposed relevance-based compression framework is implemented considering five scenarios based on the definition of relevant information from the target audience's perspective. Experimental results demonstrate the capability of the proposed approach in relevance detection. We further show that the proposed approach can achieve high compression efficiency by abstracting substantial redundant information while retaining the high quality of the relevant content.
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[545] | Markus Fox, Mario Taschwer, Klaus Schoeffmann, Pixel-Based Tool Segmentation in Cataract Surgery Videos with Mask R-CNN, In 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), IEEE, pp. 565-568, 2020.
[bib][url] [doi] [abstract]
Abstract: Automatically detecting surgical tools in recorded surgery videos is an important building block of further content-based video analysis. In ophthalmology, the results of such methods can support training and teaching of operation techniques and enable investigation of medical research questions on a dataset of recorded surgery videos. While previous methods used frame-based classification techniques to predict the presence of surgical tools — but did not localize them, we apply a recent deep-learning segmentation method (Mask R-CNN) to localize and segment surgical tools used in ophthalmic cataract surgery. We add ground-truth annotations for multi-class instance segmentation to two existing datasets of cataract surgery videos and make resulting datasets publicly available for research purposes. In the absence of comparable results from literature, we tune and evaluate the Mask R-CNN approach on these datasets for instrument segmentation/localization and achieve promising results (61\% mean average precision on 50\% intersection over union for instance segmentation, working even better for bounding box detection or binary segmentation), establishing a reasonable baseline for further research. Moreover, we experiment with common data augmentation techniques and analyze the achieved segmentation performance with respect to each class (instrument), providing evidence for future improvements of this approach.
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[544] | Hamid Mohammadi Fard, Radu Prodan, Felix Wolf, Dynamic Multi-objective Scheduling of Microservices in the Cloud, In 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC), IEEE, pp. 386-393, 2020.
[bib][url] [doi] [abstract]
Abstract: For many applications, a microservices architecture promises better performance and flexibility compared to a conventional monolithic architecture. In spite of the advantages of a microservices architecture, deploying microservices poses various challenges for service developers and providers alike. One of these challenges is the efficient placement of microservices on the cluster nodes. Improper allocation of microservices can quickly waste resource capacities and cause low system throughput. In the last few years, new technologies in orchestration frameworks, such as the possibility of multiple schedulers for pods in Kubernetes, have improved scheduling solutions of microservices but using these technologies needs to involve both the service developer and the service provider in the behavior analysis of workloads. Using memory and CPU requests specified in the service manifest, we propose a general microservices scheduling mechanism that can operate efficiently in private clusters or enterprise clouds. We model the scheduling problem as a complex variant of the knapsack problem and solve it using a multi-objective optimization approach. Our experiments show that the proposed mechanism is highly scalable and simultaneously increases utilization of both memory and CPU, which in turn leads to better throughput when compared to the state-of-the-art.
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[543] | Alireza Erfanian, Farzad Tashtarian, Reza Farahani, Christian Timmerer, Hermann Hellwagner, On Optimizing Resource Utilization in AVC-based Real-time Video Streaming, In 2020 6th IEEE Conference on Network Softwarization (NetSoft), IEEE, pp. 301-309, 2020.
[bib][url] [doi] [abstract]
Abstract: Real-time video streaming traffic and related applications have witnessed significant growth in recent years. However, this has been accompanied by some challenging issues, predominantly resource utilization. IP multicasting, as a solution to this problem, suffers from many problems. Using scalable video coding could not gain wide adoption in the industry, due to reduced compression efficiency and additional computational complexity. The emerging software-defined networking (SDN)and network function virtualization (NFV) paradigms enable re-searchers to cope with IP multicasting issues in novel ways. In this paper, by leveraging the SDN and NFV concepts, we introduce a cost-aware approach to provide advanced video coding (AVC)-based real-time video streaming services in the network. In this study, we use two types of virtualized network functions (VNFs): virtual reverse proxy (VRP) and virtual transcoder (VTF)functions. At the edge of the network, VRPs are responsible for collecting clients’ requests and sending them to an SDN controller. Then, executing a mixed-integer linear program (MILP) determines an optimal multicast tree from an appropriate set of video source servers to the optimal group of transcoders. The desired video is sent over the multicast tree. The VTFs transcode the received video segments and stream to the requested VRPs over unicast paths. To mitigate the time complexity of the proposed MILPmodel, we propose a heuristic algorithm that determines a near-optimal solution in a reasonable amount of time. Using theMiniNet emulator, we evaluate the proposed approach and show it achieves better performance in terms of cost and resource utilization in comparison with traditional multicast and unicast approaches.
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[542] | Ekrem Cetinkaya, Hadi Amirpour, Christian Timmerer, Mohammad Ghanbari, FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Learning, In 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), IEEE, pp. 87-90, 2020.
[bib][url] [doi] [abstract]
Abstract: HTTP Adaptive Streaming(HAS) is the most common approach for delivering video content over the Internet. The requirement to encode the same content at different quality levels (i.e., representations) in HAS is a challenging problem for content providers. Fast multirate encoding approaches try to accelerate this process by reusing information from previously encoded representations. In this paper, we propose to use convolutional neural networks (CNNs) to speed up the encoding of multiple representations with a specific focus on parallel encoding. In parallel encoding, the overall time-complexity is limited to the maximum time-complexity of one of the representations that are encoded in parallel. Therefore, instead of reducing the time-complexity for all representations, the highest time-complexities are reduced. Experimental results show that FaME-ML achieves significant time-complexity savings in parallel encoding scenarios(41%in average) with a slight increase in bitrate and quality degradation compared to the HEVC reference software.
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[541] | Hadi Amirpour, Ekrem Cetinkaya, Christian Timmerer, Mohammad Ghanbari, Fast Multi-rate Encoding for Adaptive HTTP Streaming, In 2020 Data Compression Conference (DCC), IEEE, 2020.
[bib][url] [doi] [abstract]
Abstract: Adaptive HTTP streaming is the preferred method to deliver multimedia content in the internet. It provides multiple representations of the same content in different qualities (i.e. bit-rates and resolutions) and allows the client to request segments from the available representations in a dynamic, adaptive way depending on its context. The growing number of representations in adaptive HTTP streaming makes encoding of one video segment at different representations a challenging task in terms of encoding time-complexity. In this paper, information of both highest and lowest quality representations are used to limit Rate Distortion Optimization (RDO) for each Coding Unit Tree (CTU) in High Efficiency Video Coding. Our proposed method first encodes the highest quality representation and consequently uses it to encode the lowest quality representation. In particular, the block structure and the selected reference frame of both highest and lowest quality representations are then used to predict and shorten the RDO process of each CTU for intermediate quality representations. Our proposed method introduces a delay of two CTUs thanks to employing parallel processing techniques. Experimental results show significant reduction in time-complexity over the reference software 38% and the state-of-the-art 10% while quality degradation is negligible.
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[540] | Hadi Amirpour, Christian Timmerer, Mohammad Ghanbari, Towards View-Aware Adaptive Streaming of Holographic Content, In 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), IEEE, 2020.
[bib][url] [doi] [abstract]
Abstract: Holography is able to reconstruct a three-dimensional structure of an object by recording full wave fields of light emitted from the object. This requires a huge amount of data to be encoded, stored, transmitted, and decoded for holographic content, making its practical usage challenging especially for bandwidth-constrained networks and memory-limited devices. In the delivery of holographic content via the internet, bandwidth wastage should be avoided to tackle high bandwidth demands of holography streaming. For real-time applications, encoding time-complexity is also a major problem. In this paper, the concept of dynamic adaptive streaming over HTTP (DASH) is extended to holography image streaming and view-aware adaptation techniques are studied. As each area of a hologram contains information of a specific view, instead of encoding and decoding the entire hologram, just the part required to render the selected view is encoded and transmitted via the network based on the users’ interactivity. Four different strategies, namely, monolithic, single view, adaptive view, and non-real time streaming strategies are explained and compared in terms of bandwidth requirements, encoding time-complexity, and bitrate overhead. Experimental results show that the view-aware methods reduce the required bandwidth for holography streaming at the cost of a bitrate increase.
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[539] | Jesus Aguilar-Armijo, Babak Taraghi, Christian Timmerer, Hermann Hellwagner, Dynamic Segment Repackaging at the Edge for HTTP Adaptive Streaming, In 2020 IEEE International Symposium on Multimedia (ISM), IEEE, pp. 17-24, 2020.
[bib] [doi] [abstract]
Abstract: Adaptive video streaming systems typically support different media delivery formats, e.g., MPEG-DASH and HLS, replicating the same content multiple times into the network. Such a diversified system results in inefficient use of storage, caching, and bandwidth resources. The Common Media Application Format (CMAF) emerges to simplify HTTP Adaptive Streaming (HAS), providing a single encoding and packaging format of segmented media content and offering the opportunities of bandwidth savings, more cache hits and less storage needed. However, CMAF is not yet supported by most devices. To solve this issue, we present a solution where we maintain the main advantages of CMAF while supporting heterogeneous devices using different media delivery formats. For that purpose, we propose to dynamically convert the content from CMAF to the desired media delivery format at an edge node. We study the bandwidth savings with our proposed approach using an analytical model and simulation, resulting in bandwidth savings of up to 20% with different media delivery format distributions. We analyze the runtime impact of the required operations on the segmented content performed in two scenarios: the classic one, with four different media delivery formats, and the proposed scenario, using CMAF-only delivery through the network. We compare both scenarios with different edge compute power assumptions. Finally, we perform experiments in a real video streaming testbed delivering MPEG-DASH using CMAF content to serve a DASH and an HLS client, performing the media conversion for the latter one.
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[538] | Cise Midoglu, Anatoliy Zabrovskiy, Ozgu Alay, Daniel Hoelbling-Inzko, Carsten Griwodz, Christian Timmerer, Docker-Based Evaluation Framework for Video Streaming QoE in Broadband Networks, In Proceedings of the 27th ACM International Conference on Multimedia, ACM New York, pp. 2288-2291, 2019.
[bib][url] [doi] |
[537] | Abdelhak Bentaleb, Christian Timmerer, Ali C. Begen, Roger Zimmermann, Bandwidth prediction on low-latency chunked streaming, In Proceedings of the 29th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, ACM New York, pp. 7-13, 2019.
[bib][url] [doi] |
[536] | Christian Timmerer, Ali C. Begen, A Journey Towards Fully Immersive Media Access, In Proceedings of the 27th ACM International Conference on Multimedia, ACM New York, pp. 2703-2705, 2019.
[bib][url] [doi] |
[535] | Jeroen van der Hooft, Tim Wauters, Filip De Turck, Christian Timmerer, Hermann Hellwagner, Towards 6dof http adaptive streaming through point cloud compression, In Proceedings of the 27th ACM International Conference on Multimedia, ACM New York, pp. 2405-2413, 2019.
[bib][url] [doi] |
[534] | Natalia Sokolova, Klaus Schöffmann, Mario Taschwer, Doris Putzgruber-Adamitsch, Yosuf El-Shabrawi, Evaluating the Generalization Performance of Instrument Classification in Cataract Surgery Videos, In Proceedings of the 26th International Conference in MultiMedia Modeling (MMM 2020) (Part II) (Wen-Huang Cheng, Junmo Kim, Wei-Ta Chu, Peng Cui, Jung-Woo Choi, Min-Chun Hu, Wesley De Neve, eds.), Springer, vol. 11962, Berlin, pp. 626-636, 2019.
[bib][url] [doi] |
[533] | Jakub Lokoc, Klaus Schöffmann, Werner Bailer, Luca Rossetto, Cathal Gurrin, Interactive Video Retrieval in the Age of Deep Learning, In Proceedings of the ACM International Conference on Multimedia Retrieval, ACM - New York, New York, NY, pp. 2-4, 2019.
[bib][url] [doi] |
[532] | Fabian Berns, Luca Rossetto, Klaus Schöffmann, Christian Beecks, George M. Awad, V3C1 Dataset: An Evaluation of Content Characteristics, In Proceedings of the ACM International Conference on Multimedia Retrieval, ACM - New York, New York, NY, pp. 334-338, 2019.
[bib][url] [doi] |
[531] | Cheng Peng, Qing Xu, Yuejun Guo, Klaus Schöffmann, Eye Movement-Based Analysis on Methodologies and Efficiency in the Process of Image Noise Evaluation, In Proceedings of the 28th International Conference on Artificial Neural Networks, Springer, Berlin, pp. 29-40, 2019.
[bib][url] [doi] |
[530] | Pal Halvorsen, Michael Riegler, Klaus Schöffmann, Medical Multimedia Systems and Applications, In Proceedings of the 27th ACM International Conference on Multimedia, ACM New York, pp. 2711-2713, 2019.
[bib][url] [doi] |
[529] | Klaus Schöffmann, Video Browser Showdown 2012-2019: A Review, In Proceedings of the International Conference on Content-Based Multimedia Indexing (CBMI'19), IEEE, Piscataway (NJ), 2019.
[bib][url] [doi] |
[528] | Raimund Schatz, Anatoliy Zabrovskiy, Christian Timmerer, Tile-based Streaming of 8K Omnidirectional Video: Subjective and Objective QoE Evaluation, In 2019 Eleventh International Conference on Qualit of Multimedia Experience (QoMEX), IEEE, New York, USA, 2019.
[bib] [pdf] [abstract]
Abstract: Omnidirectional video (ODV) streaming applica- tions are becoming increasingly popular. They enable a highly immersive experience as the user can freely choose her/his field of view within the 360-degree environment. Current deployments are fairly simple but viewport-agnostic which inevitably results in high storage/bandwidth requirements and low Quality of Experience (QoE). A promising solution is referred to as tile- based streaming which allows to have higher quality within the user’s viewport while quality outside the user’s viewport could be lower. However, empirical QoE assessment studies in this domain are still rare. Thus, this paper investigates the impact of different tile-based streaming approaches and configurations on the QoE of ODV. We present the results of a lab-based subjective evaluation in which participants evaluated 8K omnidirectional video QoE as influenced by different (i) tile-based streaming approaches (full vs. partial delivery), (ii) content types (static vs. moving camera), and (iii) tile encoding quality levels determined by different quantization parameters. Our experimental setup is character- ized by high reproducibility since relevant media delivery aspects (including the user’s head movements and dynamic tile quality adaptation) are already rendered into the respective processed video sequences. Additionally, we performed a complementary objective evaluation of the different test sequences focusing on bandwidth efficiency and objective quality metrics. The results are presented in this paper and discussed in detail which confirm that tile-based streaming of ODV improves visual quality while reducing bandwidth requirements.
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[527] | Nishant Saurabh, Julian Remmers, Dragi Kimovski, Radu Aurel Prodan, jorge G. Barbosa, Semantics-Aware Virtual Machine Image Management in IaaS Clouds, In Proceedings of the 33rd IEEE International Parallel and Distributed Processing Symposium (IPDPS) 2019, IEEE, Piscataway (NJ), pp. 418-427, 2019.
[bib][url] [doi] |