Tibor Szkaliczki

Keyframe extraction in endoscopic video

In medical endoscopy more and more surgeons archive the recorded video streams in a long-term storage. One reason for this development, which is enforced by law in some countries, is to have evidence in case of lawsuits from patients. Another more practical reason is to allow later inspection of previous procedures and also to use parts of such videos for research and for training. However, due to the dramatic amount of video data recorded in a hospital on a daily basis, it is very important to have good preview images for these videos in order to allow for quick filtering of undesired content and for easier browsing through such a video archive. Unfortunately, common shot detection and keyframe extraction methods cannot be used for that video data, because these videos contain unedited and highly similar content, especially in terms of color and texture, and no shot boundaries at all. We propose a new keyframe extraction approach for this special video domain and show that our method is significantly better than a previously proposed approach.

Knapsack problem-based piece-picking algorithms for layered content in peer-to-peer networks

The distribution of layered content over peer-to-peer networks becomes more important today as the users are consuming the content on terminals with various display capabilities and different network connections. For single-layer content distribution, the piece-picking algorithm only needs to ensure that content pieces are downloaded in time for display. When layered content is distributed over a peer-to-peer network, the piece-picking algorithm needs to be modified to ensure that the best possible quality is displayed while all desired pieces still have to be received before their deadline expires. In this paper, the piece-picking problem for layered content is analyzed and a number of piece-picking algorithms for layered content based on the solutions for the knapsack problem are presented. Furthermore, an evaluation of these algorithms is performed and possible applications are discussed.

Fast Adaptation Decision Taking for Cross-Modal Multimedia Content Adaptation

In order to enable transparent and convenient use of multimedia content across a wide range of networks and devices, content adaptation is an important issue within multimedia frameworks. The so called digital item adaptation (DIA) standard is one of the core concepts of the MPEG-21 framework that will support the adaptation of multimedia resources according to device capabilities, underlying network characteristics, and user preferences. Most multimedia adaptation engines for providing universal multimedia access (UMA) scale the content with respect to terminal capabilities and resource constraints. This paper focuses on the cross-modal adaptation decision taking process considering the user environment and terminal capabilities as well as resource limitations on the server, network, and client side. This approach represents a step toward increased universal multimedia experience (UME). Based on four different algorithms for solving this optimization process, we present an evaluation of results gained by running their implementations on different test networks

Piece Selection Algorithm for Layered Video Streaming in P2P Networks

Abstract This paper introduces the piece selection problem that arises when streaming layered video content over peer-to-peer networks. The piece selection algorithm decides periodically which pieces to request from other peers (network nodes) for download. The main goal of the piece selection algorithm is to provide the best possible quality for the available bandwidth. Our recommended solution approaches are related to the typical problems and solutions in the knapsack problem.

Placement of nodes in an adaptive distributed multimedia server

Multimedia services typically need not only huge resources but also a fairly stable level of Quality of Services. This requires server architectures that enable continuous adaptation. The Adaptive Distributed Multimedia Server (ADMS) of the University Klagenfurt is able to dynamically add and remove nodes to the actual configuration, thus realizing the offensive adaptation approach.

Piece selection algorithms for layered video streaming in P2P networks

This paper introduces the piece selection problem that arises when streaming layered video content over peer-to-peer networks. The piece selection algorithm decides periodically which pieces to request from other peers (network nodes) for download. The main goal of the piece selection algorithm is to provide the best possible quality for the available bandwidth. Our recommended solution approaches are related to the knapsack problem. In this paper, a number of layered piece picking algorithms are presented and they are compared to each other. In a competitive analysis, the presented online algorithms are compared to an optimal offline algorithm.

Incremental Placement of Nodes in a Large-Scale Adaptive Distributed Multimedia Server

An incremental algorithm is proposed to dynamically place the proxies of the Adaptive Distributed Multimedia Server (ADMS) developed at the University Klagenfurt. In order to enhance the performance of the server, the proposed algorithm examines the suitable network nodes for hosting proxies. The main benefit of the algorithm is the capability to process large problems within strict time constraints. The short running time of the algorithm enables the distributed server to adapt quickly to the changing network parameters and client demands.

Discovering Bounds of Performance of Self-Organizing Content Delivery Systems

Self-organizing methods can efficiently search, route and replicate content in complex, dynamic networks. Furthermore, they make the assumption that decisions of the nodes of the networks rely only on local information and therefore the global optimum is not known. For evaluation purposes, however, it is important to compute the global optimum to serve as a theoretical bound. In this paper we define a formal model describing the problem of content placement and use an integer linear programming (ILP) based optimization method. With this method we discover the Quality of Service (QoS) bounds of a self-organizing content delivery system. We further demonstrate how to balance between run-time complexity and accuracy of the model by applying a use case.

An evaluation of piece-picking algorithms for layered content in Bittorrent-based peer-to-peer systems

In this paper the performance of layered piece-picking algorithms for Bittorrent-based peer-to-peer systems is evaluated and compared to traditional single layer solutions. In a Bittorrent-based peer-to-peer system, the piece-picking algorithm needs to ensure that the pieces of an audiovisual content are received in time to ensure smooth playback of the content. For layered content provided in multiple qualities, the task of the algorithm becomes more complex, as it has to consider the layer of the pieces to be fetched in addition to the deadline. Thus, the goal of the layered piece-picking algorithm is to ensure that the best possible quality under the given network conditions is received while the playback is not disturbed. To illustrate the advantages of such a layered piece-picking algorithm, its performance is evaluated and compared to a single layer piece-picking algorithm. The results show that layered piece-picking algorithms can significantly improve the quality in terms of peak signal-to-noise ratio.

SEAHORSE: Generalizing an artificial hormone system algorithm to a middleware for search and delivery of information units

This paper introduces SEAHORSE (SElforganizing Artificial HORmone SystEm), a middleware that builds upon an artificial hormone system for search and delivery of information units. SEAHORSE is a generalization of an artificial hormone algorithm where information units are requested by network nodes via emitting a an artificial hormone which is propagated through the network with respect to the current network conditions. Information units are following the hormone gradient and therefore place themselves on servers where they are close to the requesting nodes. This self-organizing algorithm is robust and scalable, however, due to their complex nature, self-organizing systems are hard to configure and set up to get a desired outcome. Parameter settings that work on different scales are crucial for making the system work. Therefore, we provide a parameter study based on two use cases showing the applicability of SEAHORSE to target applications ranging from multimedia distribution at social events to information dissemination in smart electrical microgrids.