Short description: The Intel4EC workshop aims to bring together researchers, developers and practitioners from academia and industry to present their experiences, results and research progress covering architectural designs, methods and applications of AI/ML-enabled Edge-Cloud operations and services. By bringing together these research topics, Intel4EC looks forward to help the community define open standards, AI/ML benchmarks that contribute to experiment reproducibility and systematize the complete management pipeline for a myriad of Cloud-Edge operations.
Organisers: Nishant Saurabh, Zhiming Zhao and Dragi Kimovski
IEEE 35th International Symposium on Computer Based Medical Systems (CBMS), July 21-23, Shenzen, China
https://2022.cbms-conference.org
Authors: Natalia Mathá, Klaus Schoeffmann, Stephanie Sarny, Doris Putzgruber-Adamitsch and Yosuf El-Shabrawi
Abstract: In recent years, the utilization intensity and thus the demand for storing cataract surgery videos for different purposes has increased. Hospitals continuously improve their technical recording equipment, i.e., cameras, to enhance the post-operative processing efficiency of the recordings. However, afterward, the videos are stored on hospitals’ internal data servers in their original size, which leads to a massive storage consumption. In this paper, we propose a relevance-based compression scheme. First, we perform a user study with clinicians to define the relevance rates of regular cataract surgery phases. Then, we compress different phases based on the determined relevance rates, using different encoding parameters and two coding standards, namely H.264/AVC and AV1. Afterward, the medical experts evaluate the visual quality of the encoded videos. Our results show a storage-saving potential for H.264/AVC of up to 95.94% and up to 98.82% for AV1, excluding idle phases (no tools are visible).
25th International Conference on Medical Image Computing and Computer Assisted Intervention, September 18-22, 2022, Singapore
https://conferences.miccai.org/2022/en/
Authors: Negin Ghamsarian, Mario Taschwer, Raphael Sznitman, Klaus Schoeffmann
Abstract: Semantic segmentation in cataract surgery has a wide range of applications contributing to surgical outcome enhancement and clinical risk reduction. However, the varying issues in segmenting the different relevant structures in these surgeries make the designation of a unique network quite challenging. This paper proposes a semantic segmentation network termed, DeepPyram, that can deal with these challenges using three novelties: (1) a Pyramid View Fusion module which provides a varying-angle global view of the surrounding region centering at each pixel position in the input convolutional feature map; (2) a Deformable Pyramid Reception module which enables a wide deformable receptive field that can adapt to geometric transformations in the object of interest; and (3) a dedicated Pyramid Loss that adaptively supervises multi-scale semantic feature maps. Compbined we show that these can effectively boost semantic segmentation performance, especially in the case of transparency, deformability, scalability, and blunt edges in objects. We demonstrate that our approach performs at a state-of-the-art level and outperforms a number of existing methods without imposing additional trainable parameters.
For his continuous support of the International Conference on MultiMedia Modeling (MMM) and his ongoing contributions to the MMM community, yesterday Assoc.Prof. Klaus Schöffmann was awarded with the MMM’22 Community Leadership Award at the Gala Dinner in Phu Quoc, Vietnam.
This years´s “Lange Nacht der Forschung” took place on May 20, 2022. The LNDF is Austria´s most significant national research event to present the accomplishments to the broad public. ITEC was represented by three stations and involved in the station of Computer Games and Engineering, and it was a fantastic experience for everyone! We tried to make our research easily understandable for everyone.
Authors: Josef Hammer and Hermann Hellwagner, Alpen-Adria-Universität Klagenfurt
Abstract: Multi-access Edge Computing (MEC) is a central piece of 5G telecommunication systems and is essential to satisfy the challenging low-latency demands of future applications. MEC provides a cloud computing platform at the edge of the radio access network that developers can utilize for their applications. In [1] we argued that edge computing should be transparent to clients and introduced a solution to that end. This paper presents how to efficiently implement such a transparent approach, leveraging Software-Defined Networking. For high performance and scalability, our architecture focuses on three aspects: (i) a modular architecture that can easily be distributed onto multiple switches/controllers, (ii) multiple filter stages to avoid screening traffic not intended for the edge, and (iii) several strategies to keep the number of flows low to make the best use of the precious flow table memory in hardware switches. A performance evaluation is shown, with results from a real edge/fog testbed.
Keywords: 5G, Multi-Access Edge Computing, MEC, Patricia Trie, SDN, Software-Defined Networking
Title: Video Complexity Dataset (VCD)
The 13th ACM Multimedia Systems Conference (ACM MMSys 2022) Open Dataset and Software (ODS) track
June 14–17, 2022 | Athlone, Ireland
Authors: Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Vignesh V Menon (Alpen-Adria-Universität Klagenfurt), Samira Afzal (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: This paper provides an overview of the open Video Complexity Dataset (VCD) which comprises 500 Ultra High Definition (UHD) resolution test video sequences. These sequences are provided at 24 frames per second (fps) and stored online in losslessly encoded 8-bit 4:2:0 format. In this paper, all sequences are characterized by spatial and temporal complexities, rate-distortion complexity, and encoding complexity with the x264 AVC/H.264 and x265 HEVC/H.265 video encoders. The dataset is tailor-made for cutting-edge multimedia applications such as video streaming, two-pass encoding, per-title encoding, scene-cut detection, etc. Evaluations show that the dataset includes diversity in video complexities. Hence, using this dataset is recommended for training and testing video coding applications. All data have been made publicly available as part of the dataset, which can be used for various applications.
The details of VCD can be accessed online at https://vcd.itec.aau.at.
EUVIP 2022 Special Session on
“Machine Learning for Immersive Content Processing”
September, 2022, Lisbon, Portugal
Organizers:
- Hadi Amirpour, Klagenfurt University, Austria
- Christine Guillemot, INSA, France
- Christian Timmerer, Klagenfurt University, Austria
Brief description:
The importance of remote communication is becoming more and more important in particular after COVID-19 crisis. However, to bring a more realistic visual experience, more than the traditional two-dimensional (2D) interfaces we know today is required. Immersive media such as 360-degree, light fields, point cloud, ultra-high-definition, high dynamic range, etc. can fill this gap. These modalities, however, face several challenges from capture to display. Learning-based solutions show great promise and significant performance in improving traditional solutions in addressing the challenges. In this special session, we will focus on research works aimed at extending and improving the use of learning-based architectures for immersive imaging technologies.
Important dates:
Paper Submissions: 6th June, 2022
Paper Notifications: 11th July, 2022
MPEG, specifically, ISO/IEC JTC 1/SC 29/WG 3 (MPEG Systems), has been just awarded a Technology & Engineering Emmy® Award for its ground-breaking MPEG-DASH standard. Dynamic Adaptive Streaming over HTTP (DASH) is the first international de-jure standard that enables efficient streaming of video over the Internet and it has changed the entire video streaming industry including — but not limited to — on-demand, live, and low latency streaming and even for 5G and the next generation of hybrid broadcast-broadband. The first edition has been published in April 2012 and MPEG is currently working towards publishing the 5th edition demonstrating an active and lively ecosystem still being further developed and improved to address requirements and challenges for modern media transport applications and services.
This award belongs to 90+ researchers and engineers from around 60 companies all around the world who participated in the development of the MPEG-DASH standard for over 12 years.
Christian Timmerer, director of the Christian Doppler Laboratory ATHENA, chaired the evaluation of responses to the call for proposals and since that served as MPEG-DASH Ad-hoc Group (AHG) / Break-out Group (BoG) co-chair as well as co-editor for Part 2 of the standard. For a more detailed history of the MPEG-DASH standard, the interested reader is referred to Christian Timmerer’s blog post “HTTP Streaming of MPEG Media” (capturing the development of the first edition) and Nicolas Weill’s blog post “MPEG-DASH: The ABR Esperanto” (DASH timeline).
The 13th ACM Multimedia Systems Conference (ACM MMSys 2022)
June 14–17, 2022 | Athlone, Ireland
Reza Shokri Kalan (Digiturk Company, Istanbul), Reza Farahani (Alpen-Adria-Universität Klagenfurt), Emre Karsli (Digiturk Company, Istanbul), Christian Timmerer (Alpen-Adria-Universität Klagenfurt), and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)
Over-the-Top (OTT) service providers need faster, cheaper, and Digital Rights Management (DRM)-capable video streaming solutions. Recently, HTTP Adaptive Streaming (HAS) has become the dominant video delivery technology over the Internet. In HAS, videos are split into short intervals called segments, and each segment is encoded at various qualities/bitrates (i.e., representations) to adapt to the available bandwidth. Utilizing different HAS-based technologies with various segment formats imposes extra cost, complexity, and latency to the video delivery system. Enabling an integrated format for transmitting and storing segments at Content Delivery Network (CDN) servers can alleviate the aforementioned issues. To this end, MPEG Common Media Application Format (CMAF) is presented as a standard format for cost-effective and low latency streaming. However, CMAF has not been adopted by video streaming providers yet and it is incompatible with most legacy end-user players. This paper reveals some useful steps for achieving low latency live video streaming that can be implemented for non-DRM sensitive contents before jumping to CMAF technology. We first design and instantiate our testbed in a real OTT provider environment, including a heterogeneous network and clients, and then investigate the impact of changing format, segment duration, and Digital Video Recording (DVR) window length on a real live event. The results illustrate that replacing the transport stream (.ts) format with fragmented MP4 (.fMP4) and shortening segments’ duration reduces live latency significantly.
Keywords: HAS, DASH, HLS, CMAF, Live Streaming, Low Latency