Description-driven Adaptation of Media Resources

The current multimedia landscape is characterized by a significant diversity in terms of available media formats, network technologies, and device properties. This heterogeneity has resulted in a number of new challenges, such as providing universal access to multimedia content. A solution for this diversity is the use of scalable bit streams, as well as the deployment of a complementary system that is capable of adapting scalable bit streams to the constraints imposed by a particular usage environment (e.g., the limited screen resolution of a mobile device). This dissertation investigates the use of an XML-driven (Extensible Markup Language) framework for the format-independent adaptation of scalable bit streams. Using this approach, the structure of a bit stream is first translated into an XML description. In a next step, the resulting XML description is transformed to reflect a desired adaptation of the bit stream. Finally, the transformed XML description is used to create an adapted bit stream that is suited for playback in the targeted usage environment. The main contribution of this dissertation is BFlavor, a new tool for exposing the syntax of binary media resources as an XML description. Its development was inspired by two other technologies, i.e. MPEG-21 BSDL (Bitstream Syntax Description Language) and XFlavor (Formal Language for Audio-Visual Object Representation, extended with XML features). Although created from a different point of view, both languages offer solutions for translating the syntax of a media resource into an XML representation for further processing. BFlavor (BSDL+XFlavor) harmonizes the two technologies by combining their strengths and eliminating their weaknesses. The expressive power and performance of a BFlavor-based content adaptation chain, compared to tool chains entirely based on either BSDL or XFlavor, were investigated by several experiments. One series of experiments targeted the exploitation of multi-layered temporal scalability in H.264/AVC, paying particular attention to the use of sub-sequences and hierarchical coding patterns, as well as to the use of metadata messages to communicate the bit stream structure to the adaptation logic. BFlavor was the only tool to offer an elegant and practical solution for XML-driven adaptation of H.264/AVC bit streams in the temporal domain

Surveillance centric coding

PhDThe research work presented in this thesis focuses on the development of techniques specific to surveillance videos for efficient video compression with higher processing speed. The Scalable Video Coding (SVC) techniques are explored to achieve higher compression efficiency. The framework of SVC is modified to support Surveillance Centric Coding (SCC). Motion estimation techniques specific to surveillance videos are proposed in order to speed up the compression process of the SCC. The main contributions of the research work presented in this thesis are divided into two groups (i) Efficient Compression and (ii) Efficient Motion Estimation. The paradigm of Surveillance Centric Coding (SCC) is introduced, in which coding aims to achieve bit-rate optimisation and adaptation of surveillance videos for storing and transmission purposes. In the proposed approach the SCC encoder communicates with the Video Content Analysis (VCA) module that detects events of interest in video captured by the CCTV. Bit-rate optimisation and adaptation are achieved by exploiting the scalability properties of the employed codec. Time segments containing events relevant to surveillance application are encoded using high spatiotemporal resolution and quality while the irrelevant portions from the surveillance standpoint are encoded at low spatio-temporal resolution and / or quality. Thanks to the scalability of the resulting compressed bit-stream, additional bit-rate adaptation is possible; for instance for the transmission purposes. Experimental evaluation showed that significant reduction in bit-rate can be achieved by the proposed approach without loss of information relevant to surveillance applications. In addition to more optimal compression strategy, novel approaches to performing efficient motion estimation specific to surveillance videos are proposed and implemented with experimental results. A real-time background subtractor is used to detect the presence of any motion activity in the sequence. Different approaches for selective motion estimation, GOP based, Frame based and Block based, are implemented. In the former, motion estimation is performed for the whole group of pictures (GOP) only when a moving object is detected for any frame of the GOP. iii While for the Frame based approach; each frame is tested for the motion activity and consequently for selective motion estimation. The selective motion estimation approach is further explored at a lower level as Block based selective motion estimation. Experimental evaluation showed that significant reduction in computational complexity can be achieved by applying the proposed strategy. In addition to selective motion estimation, a tracker based motion estimation and fast full search using multiple reference frames has been proposed for the surveillance videos. Extensive testing on different surveillance videos shows benefits of application of proposed approaches to achieve the goals of the SCC

Covert brand recognition engages emotion-specific brain networks

Consumer goods' brands have become a major driver of consumers' choice: they have got symbolic, relational and even social properties that add substantial cultural and affective value to goods and services. Therefore, measuring the role of brands in consumers' cognitive and affective processes would be very helpful to better understand economic decision making. This work aimed at finding the neural correlates of automatic, spontaneous emotional response to brands, showing how deeply integrated are consumption symbols within the cognitive and affective processes of individuals. Functional magnetic resonance imaging (fMRI) was measured during a visual oddball paradigm consisting in the presentation of scrambled pictures as frequent stimuli, colored squares as targets, and brands and emotional pictures (selected from the International Affective Picture System [IAPS]) as emotionally-salient distractors. Affective rating of brands was assessed individually after scanning by a validated questionnaire. Results showed that, similarly to IAPS pictures, brands activated a well-defined emotional network, including amygdala and dorsolateral prefrontal cortex, highly specific of affective valence. In conclusion, this work identified the neural correlates of brands within cognitive and affective processes of consumers

Energy-aware adaptive solutions for multimedia delivery to wireless devices

The functionality of smart mobile devices is improving rapidly but these devices are limited in terms of practical use because of battery-life. This situation cannot be remedied by simply installing batteries with higher capacities in the devices. There are strict limitations in the design of a smartphone, in terms of physical space, that prohibit this “quick-fix” from being possible. The solution instead lies with the creation of an intelligent, dynamic mechanism for utilizing the hardware components on a device in an energy-efficient manner, while also maintaining the Quality of Service (QoS) requirements of the applications running on the device. This thesis proposes the following Energy-aware Adaptive Solutions (EASE): 1. BaSe-AMy: the Battery and Stream-aware Adaptive Multimedia Delivery (BaSe-AMy) algorithm assesses battery-life, network characteristics, video-stream properties and device hardware information, in order to dynamically reduce the power consumption of the device while streaming video. The algorithm computes the most efficient strategy for altering the characteristics of the stream, the playback of the video, and the hardware utilization of the device, dynamically, while meeting application’s QoS requirements. 2. PowerHop: an algorithm which assesses network conditions, device power consumption, neighboring node devices and QoS requirements to decide whether to adapt the transmission power or the number of hops that a device uses for communication. PowerHop’s ability to dynamically reduce the transmission power of the device’s Wireless Network Interface Card (WNIC) provides scope for reducing the power consumption of the device. In this case shorter transmission distances with multiple hops can be utilized to maintain network range. 3. A comprehensive survey of adaptive energy optimizations in multimedia-centric wireless devices is also provided. Additional contributions: 1. A custom video comparison tool was developed to facilitate objective assessment of streamed videos. 2. A new solution for high-accuracy mobile power logging was designed and implemented

An fMRI investigation on empathy: physical and social pain, prosocial behavior and the role of the opioid system

The work presented in this thesis collects three fMRI studies mainly focusing on empathy, i.e. the capacity to understand and/or share the emotional state of others. Empathy is central to human sociality, as it allows us to resonate with others\u2019 positive and negative feeling, and consequently adjust our behavior. Despite recent research has shed light on many feature of empathic responses, we still ignore many other aspects: for instance, which kind of computational processes are executed by empathy \u301s neural substrates, how empathic responses vary according to the type of observed experience, which neurochemical mechanisms are at the core of empathic responses, or also what is the link between empathic responses and the tendency to behave altruistically (usually referred to as \u2018prosocial behavior\u2019). The purpose of the work presented in this thesis is providing answers to some of the open questions. In Study 1 we aimed at understanding what are the neural substrates of empathy for social pain, a kind of pain that is constantly grabbing increasingly attention among social neuroscientists, and to which extent they overlap with the ones coding for physical pain. In Study 2 we investigated brain correlates of prosocial behavior by exploring functional connectivity within brain networks of participants who exhibited either a self-benefit behavior or an altruistic one in a life-threatening situation simulated in a virtual environment. In Study 3 we used a placebo manipulation on a group of participants undergoing first- hand and vicarious painful stimulations in order to observe how the supposed enhancement of endogenous opioids release would affect their behavioral and neurophysiological responses to the painful experience. Overall, the work presented in this thesis advances the knowledge on both empathy and prosociality mechanisms and opens the way for new investigations aiming at clarifying key aspects of social behavior