Christos Grecos

Optimised transmission of H.264 scalable video streams over multiple paths in mobile networks

Consumer demand for portable wireless devices such as smartphones or tablets capable of receiving high quality video content has risen sharply in recent years. This paper considers the real-time delivery of streamed H.264 Scalable Video Coding (SVC) content to users of such devices within mobile networks (e.g. situations where they are part of a group moving together on a bus or a train). We propose a novel scheme for the multipath delivery of H.264 SVC content to users in multihomed mobile networks .By implementing our scheme on a realistic testbed, we show that it offers a significant improvement in received video quality over previously proposed alternative schemes.

Removing path-switching cost in video delivery over multiple paths in mobile networks

The pace at which powerful portable wireless consumer devices such as smartphones and tablets have been adopted has not been matched by the ability of mobile networks to deliver high-quality video streams consistently to such nomadic users. In this work, we propose CMT-NEMO, a novel concurrent multipath transfer scheme for the delivery of scalable video content to users in multihomed mobile networks such as those expected to be found in public transport scenarios. Recent research in this context has shown that the single largest limiting factor for perceived video quality is the overhead introduced by path switching operations in multihomed mobile networks. CMT-NEMO eliminates the need for path switching and, by removing the associated path switching latency, provides a substantial performance improvement across a wide range of bandwidths and test sequences. An average Y-PSNR improvement of 5.9 dB is achieved when compared with previously proposed schemes.

Physiological data analysis for an emotional provoking exergame

In this work we enhance our previously developed analysis method of provoked emotions in facial expressions through the analysis of physiological data. The presented work describes the integration of electrodermal activity, respiration and temperature sensors to enhance our exergaming system for emotional provocation. The combined analysis of facial expressions and physiological data is designed to evaluate physical and cognitive stress as well as emotional reactions. The experimental setup combines a cycling game controller with a 3D virtual cycling game to provoke emotions. A designed data recording framework collects frontal videos and physiological data as well as game and controller events. In this work, we found evidence that physiological data analysis enhances the previously developed analysis method. The system is able to evaluate individual differences of an entertaining and balanced workout program.

Dual Smoothing for Marine Oil Spill Segmentation

We present a novel marine oil spill segmentation method that characterizes two smoothing modules at the label level and the pixel level separately. At the label level, we exploit the rolling guidance filter for smoothing the label cost volumes. It enables scale-aware labeling and thus alleviates the ambiguous segmentation that blurs the detailed structures of oil spills. At the pixel level, we adapt a cooperative model for smoothing higher order pixel variations, which has the potential of preserving elongated strips that often arise in oil spills. We integrate the two smoothing modules operating at different levels into an energy minimization formulation, which is referred to as dual smoothing. The coupling of the two smoothing modules enables an effective complement to each other such that the specific structures of oil spills are accurately characterized. We compute the optimal labeling of the dual-smoothing framework based on graph cuts. The proposed dual-smoothing framework is especially effective in segmenting elongated and detailed oil spills, and the experimental results demonstrate its advantages over thresholding- and graph-cut-based segmentations.

Towards More Robust Automatic Facial Expression Recognition in Smart Environments

In this paper, we provide insights towards achieving more robust automatic facial expression recognition in smart environments based on our benchmark with three labeled facial expression databases. These databases are selected to test for desktop, 3D and smart environment application scenarios. This work is meant to provide a neutral comparison and guidelines for developers and researchers interested to integrate facial emotion recognition technologies in their applications, understand its limitations and adaptation as well as enhancement strategies. We also introduce and compare three different metrics for finding the primary expression in a time window of a displayed emotion. In addition, we outline facial emotion recognition limitations and enhancements for smart environments and non-frontal setups. By providing our comparison and enhancements we hope to build a bridge from affective computing research and solution providers to application developers that like to enhance new applications by including emotion based user modeling.

Ambient Surfaces: Interactive Displays in the Informative Workspace of Co-located Scrum Teams

This paper contributes long-term findings on a custom ambient display solution (Ambient Surfaces) in the informative workspace of co-located agile (Scrum) software development teams. The study is based on the premise that displaying relevant information in a teams informative workspace is beneficial, as it readily provides information of common interest. However, existing research shows a lack of experience with software visualization tools in commercial settings. To our current knowledge, there is no study which examines ambient displays in Scrum teams with a longitudinal approach in order to address challenges in awareness and informal communication. A mixed-methods field study, which began in February 2014, is currently being conducted in cooperation with a medium-sized software company near Hamburg, Germany. Our current findings, which the following addresses, support the value of the Ambient Surfaces as a beneficial tool for Scrum teams.

A Parallel HEVC Intra Prediction Algorithm for Heterogeneous CPU+GPU Platforms

The high efficiency video coding standard provides excellent coding performance but is also very complex. Especially, the intra mode decision is very time-consuming due to the large number of available prediction modes and the flexible block partitioning scheme. In this paper, a highly parallel intra prediction algorithm for heterogeneous CPU+graphics processing unit (GPU) platforms is proposed, which accelerates the encoder dramatically. It is targeted toward high-quality high definition (HD) and ultra HD applications and utilizes prediction based on original samples (POSs), where the reference samples are generated from original pixels. This makes it possible to perform intramode prediction for all prediction blocks of a video frame concurrently. In addition, parallel-friendly cost functions are proposed which enable parallel rate distortion optimization with no synchronization overhead. A detailed statistical analysis of both POS and the proposed GPU intramethod is provided and the coding performance of the presented prototype is evaluated based on a large amount of experimental data. It is shown that the complexity of the intramode selection on the CPU is reduced by up to 78.03%. This translates to significant encoding time reductions of up to 64.52% for a single-threaded encoder and up to 94.82% in combination with wavefront parallel processing. In high bitrate ranges, average rate increases of only 2.11%-4.26% and 0.80%-2.34% are observed for the proposed high-speed and high-quality configurations, respectively. Furthermore, GPU intra is shown to be extremely efficient in lossless coding scenarios, where up to 53.37% time is saved with an average bitrate increase of only 0.55% among all test cases.

QuP: Graceful Degradation in State Propagation for DVEs

Large Distributed Virtual Environments (DVEs) combine the interactions of many users, constituting a highly complex distributed system that encompasses of a multitude of heterogeneous computers and communication over varying networks. The size of a DVE is not only measured in the spatial reach of its virtual world, but also the number of concurrent users it can provide a sufficient quality service for. The existence of hardware or software failure of these many parts must be considered the normal state of operations for any sufficiently large DVE.n We propose QuP, an approach to provide high fault tolerance when compared to many P2P approaches. QuP is also able provide the service of a large DVE service to clients connected via asymmetric or lower bandwidth networks.

In-network adaptation of SHVC video in software-defined networks

Software Defined Networks (SDN), when combined with Network Function Virtualization (NFV) represents a paradigm shift in how future networks will behave and be managed. SDN’s are expected to provide the underpinning technologies for future innovations such as 5G mobile networks and the Internet of Everything. The SDN architecture offers features that facilitate an abstracted and centralized global network view in which packet forwarding or dropping decisions are based on application flows. Software Defined Networks facilitate a wide range of network management tasks, including the adaptation of real-time video streams as they traverse the network. SHVC, the scalable extension to the recent H.265 standard is a new video encoding standard that supports ultra-high definition video streams with spatial resolutions of up to 7680×4320 and frame rates of 60fps or more. The massive increase in bandwidth required to deliver these U-HD video streams dwarfs the bandwidth requirements of current high definition (HD) video. Such large bandwidth increases pose very significant challenges for network operators. In this paper we go substantially beyond the limited number of existing implementations and proposals for video streaming in SDN’s all of which have primarily focused on traffic engineering solutions such as load balancing. By implementing and empirically evaluating an SDN enabled Media Adaptation Network Entity (MANE) we provide a valuable empirical insight into the benefits and limitations of SDN enabled video adaptation for real time video applications. The SDN-MANE is the video adaptation component of our Video Quality Assurance Manager (VQAM) SDN control plane application, which also includes an SDN monitoring component to acquire network metrics and a decision making engine using algorithms to determine the optimum adaptation strategy for any real time video application flow given the current network conditions. Our proposed VQAM application has been implemented and evaluated on an SDN allowing us to provide important benchmarks for video streaming over SDN and for SDN control plane latency.

Automatic Classification and Shift Detection of Facial Expressions in Event-Aware Smart Environments

Affective application developers often face a challenge in integrating the output of facial expression recognition (FER) software in interactive systems: although many algorithms have been proposed for FER, integrating the results of these algorithms into applications remains difficult. Due to inter-and within-subject variations further post-processing is needed. Our work addresses this problem by introducing and comparing three post-processing classification algorithms for FER output applied to an event-based interaction scheme to pinpoint the affective context within a time window. Our comparison is based on earlier published experiments with an interactive cycling simulation in which participants were provoked with game elements and their facial expression responses were analysed by all three algorithms with a human observer as reference. The three post-processing algorithms we investigate are mean fixed-window, matched filter, and Bayesian changepoint detection. In addition, we introduce a novel method for detecting fast transition of facial expressions, which we call emotional shift. The proposed detection pattern is suitable for affective applications especially in smart environments, wherever users reactions can be tied to events.