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ACM Multimedia’20: Relevance-Based Compression of Cataract Surgery Videos Using Convolutional Neural Networks

Authors: Negin Ghamsarian (Alpen-Adria-Universität Klagenfurt), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Bitmovin), Mario Taschwer (Alpen-Adria-Universität Klagenfurt), and Klaus Schöffmann (Alpen-Adria-Universität Klagenfurt)

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.

ACM International Conference on Multimedia 2020, Seattle, United States.

Link: https://2020.acmmm.org

Keywords: Video Coding, Convolutional Neural Networks, HEVC, ROI Detection, Medical Multimedia.

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BigMM’20: ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Sequences using Artificial Neural Network

The Sixth IEEE International Conference on Multimedia Big Data (BigMM 2020)
http://bigmm2020.org/

Authors: Anatoliy Zabrovskiy (Alpen-Adria-Universitat Klagenfurt), Prateek Agrawal (Alpen-Adria-Universitat Klagenfurt, Lovely Professional University), Roland Matha (Alpen-Adria-Universitat Klagenfurt), Christian Timmerer (Alpen-Adria-Universitat Klagenfurt, Bitmovin) and Radu Prodan (Alpen-Adria-Universitat Klagenfurt).

Abstract: HTTP Adaptive Streaming of video content is becoming an integral part of the Internet and accounts for the majority of today’s traffic. Although Internet bandwidth is constantly increasing, video compression technology plays an important role and the major challenge is to select and set up multiple video codecs, each with hundreds of transcoding parameters. Additionally, the transcoding speed depends directly on the selected transcoding parameters and the infrastructure used. Predicting transcoding time for multiple transcoding parameters with different codecs and processing units is a challenging task, as it depends on many factors. This paper provides a novel and considerably fast method for transcoding time prediction using video content classification and neural network prediction. Our artificial neural network (ANN) model predicts the transcoding times of video segments for state-of-the-art video codecs based on transcoding parameters and content complexity. We evaluated our method for two video codecs/implementations (AVC/x264 and HEVC/x265) as part of large-scale HTTP Adaptive Streaming services. The ANN model of our method is able to predict the transcoding time by minimizing the mean absolute error (MAE) to 1.37 and 2.67 for x264 and x265 codecs, respectively. For x264, this is an improvement of 22% compared to the state of the art.

Keywords: Transcoding time prediction, adaptive streaming, video transcoding, neural networks, video encoding, video complexity class, MPEG-DASH

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Excellence badge for Roland Mathá et al. recently published IEEE TPDS paper

The IEEE Transactions on Parallel and Distributed Systems (TPDS) paper “Simplified Workflow Simulation on Clouds based on Computation and Communication Noisiness”, published by Roland Mathá and Prof. Radu Prodan et al. got awarded the Code Reviewed Reproducibility EXCELLENCE Badge.

Christian Timmerer

Christian Timmerer elected as QoMEX Steering Committee Chair

Christian Timmerer and Peter Schelkens have been elected as Chairs of the QoMEX Steering Committee and Sebastian Möller has been elected as Treasurer.

The primary goal of the conference is to bring together leading professionals and scientists in multimedia quality and user experience from around the world. QoMEX is a conference taking place annually in early summer and guided by a steering committee.

The 12th International Conference on Quality of Multimedia Experience will be held from May 26th to 28th, 2020 in Athlone, Ireland (online). QoMEX 2020 will provide a warm welcome to leading experts from academia and industry to present and discuss current and future research on multimedia quality, quality of experience (QoE), and user experience (UX).

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Paper “Pixel-Based Tool Segmentation in Cataract Surgery Videos with Mask R-CNN” has been accepted at CBMS2020.

Our Paper “Pixel-Based Tool Segmentation in Cataract Surgery Videos with Mask R-CNN” has been accepted for publication at IEEE 33rd International Symposium on Computer Based Medical Systems (CBMS – http://cbms2020.org).
Authors: Markus Fox, Klaus Schöffmann, Mario Taschwer
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.
Pixel-Based Tool Segmentation in Cataract Surgery
Acknowledgments:
This work was funded by the FWF Austrian Science Fund under grant P 31486-N31.

Christian Timmerer
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ACM TOMM: Performance Analysis of ACTE: a Bandwidth Prediction Method for Low-Latency Chunked Streaming

Abstract: HTTP adaptive streaming with chunked transfer encoding can offer low-latency streaming without sacrificing the coding efficiency.This allows media segments to be delivered while still being packaged. However, conventional schemes often make widely inaccurate bandwidth measurements due to the presence of idle periods between the chunks and hence this is causing sub-optimal adaptation decisions. To address this issue, we earlier proposed ACTE (ABR for Chunked Transfer Encoding), a bandwidth prediction scheme for low-latency chunked streaming. While ACTE was a significant step forward, in this study we focus on two still remaining open areas, namely (i) quantifying the impact of encoding parameters, including chunk and segment durations, bitrate levels, minimum interval between IDR-frames and frame rate onACTE, and (ii) exploring the impact of video content complexity on ACTE. We thoroughly investigate these questions and report on our findings. We also discuss some additional issues that arise in the context of pursuing very low latency HTTP video streaming.

Authors: Abdelhak Bentaleb (National University of Singapore), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Bitmovin), Ali C. Begen (Ozyegin University, Networked Media), Roger Zimmermann (National University of Singapore)

Keywords: HAS; ABR; DASH; CMAF; low-latency; HTTP chunked transfer encoding; bandwidth measurement and prediction; RLS; encoding parameters; FFmpeg

Christian Timmerer
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QoMEX’20: Objective and Subjective QoE Evaluation for Adaptive Point Cloud Streaming

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.

Authors:Jeroen van der Hooft (Ghent University), Maria Torres Vega (Ghent University), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Bitmovin), Ali C. Begen (Ozyegin University, Networked Media), Filip De Turck (Ghent University), Raimund Schatz (Alpen-Adria Universität Klagenfurt & AIT Austrian Institute of Technology, Austria)

Keywords: Volumetric Media; HTTP Adaptive Streaming; 6DoF; MPEG V-PCC; QoE Assessment; Objective Metrics

International Conference on Quality of Multimedia Experience (QoMEX)
May 26-28, 2020, Athlone, Ireland
http://qomex2020.ie/

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Paper accepted in IEEE Transactions on Parallel and Distributed Systems journal

The manuscript “The Workflow Trace Archive: Open-Access Data from Public and Private Computing Infrastructures” has been accepted for publication in the A* ranked IEEE Transactions on Parallel and Distributed Systems (TPDS) journal.

Authors: Laurens Versluis, Roland Mathá, Sacheendra Talluri, Tim Hegeman, Radu Prodan, Ewa Deelman, and Alexandru Iosup

Abstract: Realistic, relevant, and reproducible experiments often need input traces collected from real-world environments. We focus in this work on traces of workflows—common in datacenters, clouds, and HPC infrastructures. We show that the state-of-the-art in using workflow-traces raises important issues: (1) the use of realistic traces is infrequent, and (2) the use of realistic, open-access traces even more so. Alleviating these issues, we introduce the Workflow Trace Archive (WTA), an open-access archive of workflow traces from diverse computing infrastructures and tooling to parse, validate, and analyze traces. The WTA includes >48 million workflows captured from >10 computing infrastructures, representing a broad diversity of trace domains and characteristics. To emphasize the importance of trace diversity, we characterize the WTA contents and analyze in simulation the impact of trace diversity on experiment results. Our results indicate significant differences in characteristics, properties, and workflow structures between workload sources, domains, and fields.

Acknowledgments: This work is supported by the projects Vidi MagnaData, Commit, the European Union’s Horizon 2020 Research and Innovation Programme, grant agreement number 801091 “ASPIDE”, and the National Science Foundation award number 1664162.

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A paper “On Optimizing Resource Utilization in AVC-based Real-time Video Streaming” was accepted at the IEEE Conference on Network Softwarization

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.

Authors: Alireza Erfanian, Farzad Tashtarian, Reza Farahani, Christian Timmerer, Hermann Hellwagner

IEEE Conference on Network Softwarization 29 June-3 July 2020 // Ghent, Belgium http://netsoft2020.netsoft-ieee.org

Keywords—Dynamic Adaptive Streaming over HTTP (DASH), Real-time Video Streaming, Software Defined Networking (SDN), Video Transcoding, Network Function Virtualization (NFV).

Natalia Sokolova
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A 1-page abstract “Pixel-Based Iris and Pupil Segmentation in Cataract Surgery Videos Using Mask R-CNN” was accepted at the workshop of the International Symposium on Biomedical Imaging

The 1-page abstract “Pixel-Based Iris and Pupil Segmentation in Cataract Surgery Videos Using Mask R-CNN” was accepted at the workshop “Deep Learning for Biomedical Image Reconstruction” of the International Symposium on Biomedical Imaging that will take place in Iowa-City, Iowa, USA, 3-7 April.

Authors:
Natalia Sokolova, Mario Taschwer, Klaus Schoeffmann

Acknowledment:
This work was funded by the FWF Austrian Science Fund under grant P 31486-N31