Browsing by Author "Fernandes, Rui"
Now showing 1 - 5 of 5
Results Per Page
Sort Options
- Experiments of a new generation points strategy in a multilocal methodPublication . Baniya, Amulya; Fernandes, Rui; Fernandes, Florbela P.Nonlinear programming problems appear frequently in industrial/real problems. It is important to obtain its solution in the lowest time possible, since the company could benefit from this. Taking this into account, a derivative free method (MCSFilter method) is addressed with a different strategy to generate the initial points to start each local search. The idea is to spread more the points so that the code execution will require a shortest amount of time when compared with the MCSFilter method execution. Some experiments were performed with simple bounds problems, equality and inequality constraint problems, chosen from a set of well known nonlinear problems. The results obtained were encouraging and with the new strategy the method needs less time to obtain the global solution.
- Implementation of a 2D/3D multimedia content adaptation decision enginePublication . Fernandes, Rui; Andrade, M.T.Multimedia content consumption has become very popular due to several factors, among which the amount of content available online and the ubiquity of network connectivity. In fact nowadays everyone can be a content producer and be almost permanently connected to the Internet, thus having the possibility to consume content anywhere, anytime. However, content may present itself in a multitude of formats and networks and terminals may offer very dissimilar transport and consumption capabilities, both along time and space. Accordingly, it often happens that the delivery and/or consumption environments do not offer sufficient or adequate resources to allow the remote access to and consumption of original high quality content. Content adaptation techniques establish means to surpass those impossibilities, allowing content delivery to the user regardless of existing constraints. Since there are several ways to adapt multimedia content, to which different users may react differently, the adaptation engine in charge of deciding the type of adaptation to perform, should ideally be driven with the aim of providing the best Quality of Experience (QoE) to the user [1]. To achieve the best possible outcome, the engine should take into consideration the characteristics of every entity and person involved in the content consumption process, which includes, (1) the multimedia content itself, (2) the transport/access network characteristics, (3) the terminal device characteristics and the (4) user preferences. To take into consideration the multimedia content it is necessary to characterise it and eventually to classify it into a set of limited but meaningful classes. Different metrics were implemented to tackle the content characteristics identification/classification. These were mainly focused on the spatial and temporal complexity classification of the content. The networks characteristics establish the restrictions the adaptation decision algorithm has to obey. This is also true for the device capabilities/characteristics. The user preferences are the subjective element that may establish, for one consumption scenario, different QoE, with the use of the same adaptations, for different users. To investigate this parameter, a subjective quality evaluation was performed. Different contents were generated and classified using the metrics devised to perform that task [2] and, based on this classification, four contents with different characteristics were chosen to be presented to the users. Several bitrates were used to simulate different network conditions, three different types of terminals were used (display, tablet and smartphone) and three adaptations were executed over the original contents, namely, spatial, temporal and quality alterations of the content. The users were asked to classify each presented version on a qualitative scale [3]. The obtained results indicate, as expected, the existence of different users profiles and that the (4) users preferences are dependent of the other three factors: (1), (2) and (3). Results from this subjective experiment are now under analysis to generate these user profiles using an approach that complements Multiple Correspondence Analysis and Cluster Identification. All these characteristics are to be used by a learning algorithm to define the cost of executing a certain adaptation, whenever a certain content is being consumed under specific conditions by a certain user. These costs are then fed to the adaptation decision engine, already implemented through a Markov Decision Process (MDP) to define the final adaptation decision. References: [1] “Definition of quality of experience,” TD109rev2 (PLEN/12), International Telecommunications Union, ITU-T Study Group 12, 2007. [2] J. Korhonen, U. Reiter, and A Ukhanova, “Frame rate versus spatial quality: Which video characteristics do matter?,” in Visual Communications and Image Processing (VCIP), 2013, Nov 2013, pp. 1–6. [3] ITU-R Recommendation BT.500-13, “Methodology for the subjective assessment of the quality of television pictures,” Tech. Rep. BT.500-13, International Telecommunications Union, January 2012.
- Knowledge and context-based strategies for 3D video content adaptation decisionPublication . Fernandes, Rui; Andrade, TeresaNowadays, there exists a rich multimedia content scenario where the coexistence of different media types and formats is a reality. For instance, 3D content can be implemented through different coding approaches and is becoming more relevant due to the introduction of encoding architectures, such as the Multiview Video Coding (MVC), that encodes several different views of the scene and allows displays to generate the depth impression without the use of glasses. Furthermore, several transport/access networks, with different dynamic characteristics, can be used to deliver the content to the user, who, in turn, can consume it over a great diversity of client devices, each with its own capabilities. In this scenario, content adaptation deals with different resources constraints, while changes the multimedia content to meet the users expectation, delivering the best quality possible. There are several approaches to perform adaptation such as transcoding, selection, summarization, region of interest or transmoding, among others. This implies the need, when several adaptations are available, to define which adaptation or set of adaptations to execute over the content, this is, to take an adaptation decision. For 3D content, namely MVC, our approach defines states, based on variables that describe the content characteristics, that, in turn, are used together with the available adaptations to generate a map state, over which a Markov Decision Process is devised and solved to define the optimal policy of adaptation execution. 1
- Multimedia content classification metrics for content adaptationPublication . Fernandes, Rui; Andrade, M.T.Multimedia content consumption is very popular nowadays. However, not every content can be consumed in its original format: the combination of content, transport and access networks, consumption device and usage environment characteristics may all pose restrictions to that purpose. One way to provide the best possible quality to the user is to adapt the content according to these restrictions as well as user preferences. This adaptation stage can be best executed if knowledge about the content is known a-priori. In order to provide this knowledge we classify the content based on metrics to define its temporal and spatial complexity. The temporal complexity classification is based on the Motion Vectors of the predictive encoded frames and on the difference between frames. The spatial complexity classification is based on different implementations of an edge detection algorithm and an image activity measure.
- Multimedia content classification metrics for content adaptationPublication . Fernandes, Rui; Andrade, M.T.Multimedia content consumption is very popular nowadays. However, not every content can be consumed in its original format: the combination of content, transport and access networks, consumption device and usage environment characteristics may all pose restrictions to that purpose. One way to provide the best possible quality to the user is to adapt the content according to these restrictions as well as user preferences. This adaptation stage can be best executed if knowledge about the content is known à- priori. In order to provide this knowledge we classify the content based on metrics to define its temporal and spatial complexity. The temporal complexity classification is based on the Motion Vectors of the predictive frames of the content, and the spatial complexity classification is based on an edge detection algorithm and an image activity measure.