GWAP as a Tool to Analyze, Design, and Test Geo-Social Systems

In this chapter, the authors discuss the state-of-the-art of Geo-Social systems and Recommender systems, which are becoming extremely popular for users accessing social media trough mobile devices. Moreover, they introduce a general framework based on the interaction among those systems and the “Game With A Purpose” (GWAP) paradigm. The proposed framework/platform can help researchers to understand geo-social dynamics in order to design and test new services, such as recommenders of places of interest for tourists, real-time traffic information systems, personalized suggestions of social events, and so forth. To target the governance of such complexity, relevant data must be collected by the investigators, shared with the community, and analyzed to find dynamical patterns that correlate spatial-temporal information with the user’s preferences and objectives. The authors argue that the GWAP approach can be exploited to successfully satisfy many of these tasks.

Region-of-Interest Processing and Coding Techniques

This chapter comprehensively covers the topic of the Region-of-Interest (ROI) processing and coding for multimedia applications. The variety of end-user devices with different capabilities, ranging from cell phones with small screens and restricted processing power to high-end PCs with high-definition displays, have stimulated significant interest in effective technologies for video adaptation. Therefore, the authors make a special emphasis on the ROI processing and coding with regard to the relatively new H.264/SVC (Scalable Video Coding) standard, which have introduced various scalability domains, such as spatial, temporal, and fidelity (SNR/quality) domains. The authors’ observations and conclusions are supported by a variety of experimental results, which are compared to the conventional Joint Scalable Video Model (JSVM).

Context-Aware Medical Image Retrieval for Improved Dementia Diagnosis

Progress in medical imaging technology together with the increasing demand for confirming a diagnostic decision with objective, repeatable, and reliable measures for improved healthcare have multiplied the number of digital medical images that need to be processed, stored, managed, and searched. Comparison of multiple patients, their pathologies, and progresses by using image search systems may largely contribute to improved diagnosis and education of medical students and residents. Supporting image content information with contextual knowledge will lead to increased reliability, robustness, and accuracy in search results. To this end, the authors present an image search system that permits search by a multitude of image features (content), and demographics, patient’s medical history, clinical data, and ontologies (context). Moreover, they validate the system’s added value in dementia diagnosis via evaluations on publicly available image databases.

Multimedia Social Networks and E-Learning

The purpose of this chapter is to investigate Multimedia Social Networks and e-Learning, and the relevant research in these areas. Multimedia Social Networks in e-Learning is an important and evolving study area, since an understanding of the technologies involved as well as an understanding of how the students communicate in online social networks are necessary in order to accurately analyze them. The chapter begins by introducing Multimedia Social Networks and Online Communities. Following this, the key players of e-Learning in Multimedia Social Networks are presented, including a discussion of the different roles that the students take. Furthermore, Social Interaction research is presented concentrating on such important areas as factors that influence social interaction, peer support, student-centered learning, collaboration, and the effect of interaction on learning. The last section of the chapter deals with the various methods and frameworks for analyzing multimedia social networks in e-Learning communities.

Provisioning Converged Applications and Services via the Cloud

The cloud is becoming an atmosphere to store huge data and deploy massive applications. Using virtualization technologies, it is economical and feasible to provide testbeds in the cloud. The convergence of Next Generation (NG) networks and Internet-based applications may result in the deployment of future rich Internet applications and services in the cloud. This chapter shows the migration of mobility-enabled services to the cloud. It presents a SIP-based hybrid architecture for Web session mobility that offers content sharing and session handoff between Web browsers. The implemented system has recently evolved to a framework for developing different kinds of converged services over the Internet, which are similar to services offered by Google Wave and existing telephony Application Programming Interfaces (APIs). In addition, the work in this chapter is compared with those similar technologies. Lastly, the authors show efforts to migrate the SIP/HTTP application server to the cloud, which was necessitated by the need to include more functionalities (i.e., QoS and rich media support) as well as to provide large-scale deployment in a multi-domain scenario.

Intelligent Approaches for Adaptation and Distribution of Personalized Multimedia Content

Telecommunication operators need to deliver their clients not only new profitable services, but also good quality and interactive content. Some of this content, such as advertisements, generate revenues, while other contents generate revenues associated to a service, such as Video on Demand (VoD). One of the main concerns for current multimedia platforms is therefore the provisioning of content to end-users that generates revenue. Alternatives currently being explored include user-content generation as the content source (the prosumer model). However, a large source of revenue has pretty much been neglected, which corresponds to the capability of transforming, adapting content produced either by Content Providers (CPs) or by the end-user according to different categories, such as client location, personal settings, or business considerations, and to distribute such modified content. This chapter discusses and addresses this gap, proposing a content customization and distribution system for changing content consumption, by adapting content according to target end-user profiles (such as end-user personal tastes or its local social or geographic community). The aim is to give CPs ways to allow users and/or Service Providers (SPs) to configure contents according to different criteria, improving users’ quality of experience and SPs’ revenues generation, and to possibly charge users and SPs (e.g. advertisers) for such functionalities. The authors propose to employ artificial intelligence techniques, such as mixture of Gaussians, to learn the functional constraints faced by people, objects, or even scenes on a movie stream in order to support the content modification process. The solutions reported will allow SPs to provide the end-user with automatic ways to adapt and configure the (on-line, live) content to their tastes—and even more—to manipulate the content of live (or off-line) video streams (in the way that photo editing did for images or video editing, to a certain extent, did for off-line videos).

Recent Advances in Computational Complexity Techniques for Video Coding Applications

The computational complexity issue is critical for present and future video applications implemented by relatively new video coding standards, such as the H.264/AVC (Advanced Video Coding), which has a large number of coding modes. One of the main reasons for the importance of providing an efficient complexity control in video coding applications is a strong need to decrease the encoding/decoding computational complexity, especially when the encoding and/or decoding devices are resource-limited, such as portable devices. In turn, efficient complexity control enables reducing the video coding processing time and enables saving power resources during the encoding and/or decoding process. Since the recent dramatic progress in the development of multimedia technologies has made portable devices widespread everywhere, especially in order to provide or receive real-time video contents, the need to enhance the computational complexity control in video coding applications is expected to be further significantly increased as a function of the dramatic increase in the mobile/portable device penetration into the every-day life environment. In this chapter, the authors perform a comprehensive review of the recent advances in computational complexity techniques for video coding applications. This chapter will not only summarize the recent advances in this field, but will also provide explicit directions for the design of the future complexity-aware video coding applications.

The Realisation of Online Music Services through Intelligent Computing

This chapter presents an extensive, although non-exhaustive, study on existing Online Music Services (OMSs), which aims at identifying two principal characteristics: (1) the functionalities and interaction capabilities offered to their end-users; and (2) the tools of computational intelligence employed so as to enable these functionalities. The study is predominantly motivated by the ever-growing impact of Music Information Retrieval (MIR) research on the music industry, as new approaches for knowledge acquisition are rapidly integrated in existing online services targeting music consumers, musicians, as well as the music industry. Since MIR is inherently addressing user needs in music aggregation and distribution, the first part of the chapter is dedicated to illustrating user functionalities and accordingly classifying existing OMSs. The second part of the chapter focuses on musical semantics, different methods for harvesting them, and approaches for exploiting them in existing OMSs. Finally, the chapter attempts to foresee functionalities of future OMSs enabled by forthcoming MIR achievements.

PriorityQoE

In the next generation of mobile network services, there will be the provision of multimedia services with the desired quality for wireless networks. In the future Internet, an integrated platform of cloud services will be made available within the XaaS (X-as-a-Service) paradigm. In the light of this evidence, the focal point of this study is an area that is very important to analyze, which is how to ensure a satisfactory Quality of Experience (QoE) for applications with video streaming. This chapter shows the PriorityQoE tool, which employs a methodology to establish a hierarchy for video streaming packets that are based on QoE objective metrics. It also outlines an intelligent mechanism for packet discard together with the PriorityQoE. The results of the performance evaluation of the tools showed that the effects of congestion on the network through the QoE of the video streaming were reduced. The QoE mediations were carried out by considering the knowledge of three QoE objective metrics (SSIM, VQM, and PSNR). The evaluation was conducted by means of a simulation of the transmission of multimedia content in IEEE 802.11 networking standards. The tools showed a better buffer handling and discarded the packet that least degrades the QoE of the video streaming.

Techniques and Tools for Adaptive Video Streaming

Adaptive video streaming is becoming increasingly necessary as quality expectations rise, while congestion persists and the extension of the Internet to mobile access creates new sources of packet loss. This chapter considers several techniques for adaptive video streaming including live HTTP streaming, bitrate transcoding, scalable video coding, and rate controllers. It also includes additional case studies of congestion control over the wired Internet using fuzzy logic, statistical multiplexing to adapt constant bitrate streams to the bandwidth capacity, and adaptive error correction for the mobile Internet. To guide the reader, the chapter makes a number of comparisons between the main techniques, for example explaining why currently per-encoded video may be better-streamed using adaptive simulcast than by transcoding or scalable video coding.