Abstract: More and more immersive media applications and services are emerging on the market, but lack international standards to enable interoperability. This article provides an overview about ongoing standardization efforts in this exciting domain and highlights open research and standardization issues.

Abstract: In the past decade we observed the transition from push-based, fully managed media streaming to pull-based, unmanaged adaptive HTTP streaming thanks to enhancements in media compression, network capacity, and client capabilities. Adaptive media players, specifically their algorithms, have been subject to research for a long time and lead to various approaches documented in the literature. In the past years we witnessed more and more commercial deployments taking into account findings presented in scientific papers but a quantitative evaluation and assessments of its performance is missing. In this paper, we propose means for the automated performance evaluation of commercially deployed adaptive media players with respect to i) objective, well-known metrics, such as bitrate, stalls, startup delay and ii) derived/calculated metrics (instability, inefficiency, average bitrate) previously proposed in the literature. Additionally, we propose a new metric (Bandwidth index) to measure the effectiveness of bandwidth utilization and together with existing QoE models for adaptive HTTP streaming (focusing on stalls, startup delay) we demonstrate its usefulness in this domain.

Abstract: Medical case retrieval (MCR) is defined as a multimedia retrieval problem, where the document collection consists of medical case descriptions that pertain to particular diseases, patients' histories, or other entities of biomedical knowledge. Case descriptions are multimedia documents containing textual and visual modalities (images). A query may consist of a textual description of patient's symptoms and related diagnostic images. This thesis proposes and evaluates methods that aim at improving MCR effectiveness over the baseline of fulltext retrieval. We hypothesize that this objective can be achieved by utilizing controlled vocabularies of biomedical concepts for query expansion and concept-based retrieval. The latter represents case descriptions and queries as vectors of biomedical concepts, which may be generated automatically from textual and/or visual modalities by concept mapping algorithms. We propose a multimodal retrieval framework for MCR by late fusion of text-based retrieval (including query expansion) and concept-based retrieval and show that retrieval effectiveness can be improved by 49% using linear fusion of practical component retrieval systems. The potential of further improvement is experimentally estimated as a 166% increase of effectiveness over fulltext retrieval using query-adaptive fusion of ideal component retrieval systems. Additional contributions of this thesis include the proposal and comparative evaluation of methods for concept mapping, query and document expansion, and automatic classification and separation of compound figures found in case descriptions.

Abstract: In hospitals all around the world, medical multimedia information systems have gained high importance over the last few years. One of the reasons is that an increasing number of interventions are performed in a minimally invasive way. These endoscopic inspections and surgeries are performed with a tiny camera -- the endoscope -- which produces a video signal that is used to control the intervention. Apart from the viewing purpose, the video signal is also used for automatic content analysis during the intervention as well as for post-surgical usage, such as communicating operation techniques, planning future interventions, and medical forensics. Another reason is video documentation, which is even enforced by law in some countries. The problem, however, is the sheer amount of unstructured medical videos that are added to the multimedia archive on a daily basis. Without proper management and a multimedia information system, the medical videos cannot be used efficiently for post-surgical scenarios. It is therefore already foreseeable that medical multimedia information systems will gain even more attraction in the next few years. In this tutorial we will introduce the audience to this challenging new field, describe the domain-specific characteristics and challenges of medical multimedia data, introduce related use cases, and talk about existing works -- contributed by the medical imaging and robotics community, but also already partly from the multimedia community -- as well as the many open issues and challenges that bear high research potential.

Abstract: This extended demo paper summarizes our interface used for the Video Browser Showdown (VBS) 2017 competition, where visual and textual known-item search (KIS) tasks, as well as ad-hoc video search (AVS) tasks in a 600-h video archive need to be solved interactively. To this end, we propose a very flexible distributed video search system that combines many ideas of related work in a novel and collaborative way, such that several users can work together and explore the video archive in a complementary manner. The main interface is a perspective Feature Map, which shows keyframes of shots arranged according to a selected content similarity feature (e.g., color, motion, semantic concepts, etc.). This Feature Map is accompanied by additional views, which allow users to search and filter according to a particular content feature. For collaboration of several users we provide a cooperative heatmap that shows a synchronized view of inspection actions of all users. Moreover, we use collaborative re-ranking of shots (in specific views) based on retrieved results of other users.

Abstract: Due to increasing possibilities to create digital video, we are facing the emergence of large video archives that are made accessible either online or offline. Though a lot of research has been spent on video retrieval tools and methods, which allow for automatic search in videos, still the performance of automatic video retrieval is far from optimal. At the same time, the organization of personal data is receiving increasing research attention due to the challenges that are faced in gathering, enriching, searching and visualizing this data. Given the increasing quantities of personal data being gathered by individuals, the concept of a heterogeneous personal digital libraries of rich multimedia and sensory content for every individual is becoming a reality. Despite the differences between video archives and personal lifelogging libraries, we are facing very similar challenges when accessing these multimedia repositories. For example, users will struggle to find the information they are looking for in either collection if they are not able to formulate their search needs through a query. In this tutorial we discussed (i) proposed solutions for improved video & lifelog content navigation, (ii) typical interaction of content-based querying features, and (iii) advanced content visualization methods. Moreover, we discussed and demonstrate interactive video & lifelog search systems and ways to evaluate their performance.

Abstract: Currently, we witness dramatically increasing interest in immersive media technologies like Virtual Reality (VR), particularly in omnidirectional video (OV) streaming. Omnidirectional (also called 360-degree) videos are panoramic spherical videos in which the user can look around during playback and which therefore can be understood as hybrids between traditional movie streaming and interactive VR worlds. Unfortunately, streaming this kind of content is extremely bandwidth intensive (compared to traditional 2D video) and therefore, Quality of Experience (QoE) tends to deteriorate significantly in absence of continuous optimal bandwidth conditions. In this paper, we present a first approach towards subjective QoE assessment for omnidirectional video (OV) streaming. We present the results of a lab study on the QoE impact of stalling in the context of OV streaming using head-mounted displays (HMDs). Our findings show that subjective testing for immersive media like OV is not trivial, with even simple cases like stalling leading to unexpected results. After a discussion of characteristic pitfalls and lessons learned, we provide a a set of recommendations for upcoming OV assessment studies.

Abstract: Forecasts predict that Internet traffic will continue to grow in the near future. A huge share of this traffic is caused by multimedia streaming. The Quality of Experience (QoE) of such streaming services is an important aspect and in most cases the goal is to maximize the bit rate which -- in some cases -- conflicts with the requirements of both consumers and providers. For example, in mobile environments users may prefer a lower bit rate to come along with their data plan. Likewise, providers aim at minimizing bandwidth usage in order to reduce costs by transmitting less data to users while maintaining a high QoE. Today's adaptive video streaming services try to serve users with the highest bit rates which consequently results in high QoE. In practice, however, some of these high bit rate representations may not differ significantly in terms of perceived video quality compared to lower bit rate representations. In this paper, we present a novel approach to determine the statistically indifferent quality variation (SIQV) of adjacent video representations for adaptive video streaming services by adopting standard objective quality metrics and existing QoE models. In particular, whenever the quality variation between adjacent representations is imperceptible from a statistical point of view, the representation with higher bit rate can be substituted with a lower bit rate representation. As expected, this approach results in savings with respect to bandwidth consumption while still providing a high QoE for users. The approach is evaluated subjectively with a crowdsourcing study. Additionally, we highlight the benefits of our approach, by providing a case study that extrapolates possible savings for providers.

Abstract: This paper describes our approach used for the fully automatic and manually assisted Ad-hoc Video Search (AVS) task for TRECVID 2017. We focus on the combination of different convolutional neural network models and query optimization. Each of this model focus on a specific query part, which could be, e.g., location, objects, or the wide-ranging ImageNet classes. All classification results are collected in different combinations in Lucene indixes. For the manually assisted run we use a junk filter and different query optimization methods.

Abstract: Forwarding decisions in classical IP-based networks are predetermined by routing. This is necessary to avoid loops, inhibiting opportunities to implement an adaptive and intelligent forwarding plane. Consequently, content distribution efficiency is reduced due to a lack of inherent multi-path transmission. In Named Data Networking (NDN) instead, routing shall hold a supporting role to forwarding, providing sufficient potential to enhance content dissemination at the forwarding plane. In this paper we design, implement, and evaluate a novel probability-based forwarding strategy, called Stochastic Adaptive Forwarding (SAF) for NDN. SAF imitates a self-adjusting water pipe system, intelligently guiding and distributing Interests through network crossings circumventing link failures and bottlenecks. Just as real pipe systems, SAF employs overpressure valves enabling congested nodes to lower pressure autonomously. Through an implicit feedback mechanism it is ensured that the fraction of the traffic forwarded via congested nodes decreases. By conducting simulations we show that our approach outperforms existing forwarding strategies in terms of the Interest satisfaction ratio in the majority of the evaluated scenarios. This is achieved by extensive utilization of NDN's multipath and content-lookup capabilities without relying on the routing plane. SAF explores the local environment by redirecting requests that are likely to be dropped anyway. This enables SAF to identify new paths to the content origin or to cached replicas, circumventing link failures and resource shortages without relying on routing updates.

Abstract: The emergence of Information-Centric Networking (ICN) provides considerable opportunities for context-aware data distribution in the network's forwarding plane. While packet forwarding in classical IP-based networks is basically predetermined by routing, ICN foresees an adaptive forwarding plane considering the requirements of network applications. As research in this area is still at an early stage, most of the work so far focused on providing the basic functionality, rather than on considering the available context information to improve Quality of Service (QoS). This article investigates to which extent existing forwarding strategies take account of the available context information and can therefore increase service quality. The article examines a typical scenario encompassing different user applications (Voice over IP, video streaming, and classical data transfer) with varying demands (context), and evaluates how well the applications' requirements are met by the existing strategies.

Abstract: Over the last decades, progress in medical technology and imaging technology enabled the technique of minimally invasive surgery. In addition, multimedia technologies allow for retrospective analyses of surgeries. The accumulated videos and images allow for a speed-up in documentation, easier medical case assessment across surgeons, training young surgeons, as well as they find the usage in medical research. Considering a surgery lasting for hours of routine work, surgeons only need to see short video segments of interest to assess a case. Surgeons do not have the time to manually extract video sequences of their surgeries from their big multimedia databases as they do not have the resources for this time-consuming task. The thesis deals with the questions of how to semantically classify video frames using Convolutional Neural Networks into different semantic concepts of surgical actions and anatomical structures. In order to achieve this goal, the capabilities of predefined CNN architectures and transfer learning in the laparoscopic video domain are investigated. The results are expected to improve by domain-specific adaptation of the CNN input layers, i.e. by fusion of the image with motion and relevance information. Finally, the thesis investigates to what extent surgeons' needs are covered with the proposed extraction of relevant scenes.

Abstract: The field of similarity based image retrieval has experienced a game changer lately. Hand crafted image features have been vastly outperformed by machine learning based approaches. Deep learning methods are very good at finding optimal features for a domain, given enough data is available to learn from. However, hand crafted features are still means to an end in domains, where the data either is not freely available, i.e. because it violates privacy, where there are commercial concerns, or where it cannot be transmitted, i.e. due to bandwidth limitations. Moreover, we have to rely on hand crafted methods whenever neural networks cannot be trained effectively, e.g. if there is not enough training data. In this paper, we investigate a particular approach to combine hand crafted features and deep learning to (i) achieve early fusion of off the shelf handcrafted global image features and (ii) reduce the overall number of dimensions to combine both worlds. This method allows for fast image retrieval in domains, where training data is sparse.

Abstract: In the last decade, advances in endoscopic surgery resulted in vast amounts of video data which is used for documentation, analysis, and education purposes. In order to find video scenes relevant for aforementioned purposes, physicians manually search and annotate hours of endoscopic surgery videos. This process is tedious and time-consuming, thus motivating the (semi-)automatic annotation of such surgery videos. In this work, we want to investigate whether the single-frame model for semantic surgery shot classification is feasible and useful in practice. We approach this problem by further training of AlexNet, an already pre-trained CNN architecture. Thus, we are able to transfer knowledge gathered from the Imagenet database to the medical use case of shot classification in endoscopic surgery videos. We annotate hours of endoscopic surgery videos for training and testing data. Our results imply that the CNN-based single-frame classification approach is able to provide useful suggestions to medical experts while annotating video scenes. Hence, the annotation process is consequently improved. Future work shall consider the evaluation of more sophisticated classification methods incorporating the temporal video dimension, which is expected to improve on the baseline evaluation done in this work.

Abstract: In this working note, we describe our approach to gastrointestinal disease and anatomical landmark classification for the Medico task at MediaEval 2017. We propose an inception-like CNN architecture and a fixed-crop data augmentation scheme for training and testing. The architecture is based on GoogLeNet and designed to keep the number of trainable parameters and its computational overhead small. Preliminary experiments show that the architecture is able to learn the classification problem from scratch using a tiny fraction of the provided training data only.