Reliable and Energy-Efficient Hybrid Screen Mirroring Multicast System

This paper presents a reliable and energy-efficient hybrid screen mirroring multicast system for sharing high-quality real-time multimedia service with adjacent mobile devices over WiFi network. The proposed system employs overhearing-based multicast transmission scheme with Raptor codes and NACK-based retransmission to overcome well-known WiFi multicast problems such as low transmission rate and high packet loss rate. Furthermore, to save energy on mobile devices, the proposed system not only shapes the screen mirroring traffic, but also determines the target sink device and Raptor encoding parameters such as the number of source symbols, symbol size, and code rate while considering the energy consumption and processing delay of the Raptor encoding and decoding processes. The proposed system is fully implemented in Linux-based single board computers and examined in real WiFi network. Compared to existing systems, the proposed system can achieve good energy efficiency while providing a high-quality screen mirroring service.11Nsciescopu

Low-complexity wavelet-based scalable image & video coding for home-use surveillance

We study scalable image and video coding for the surveillance of rooms and personal environments based on inexpensive cameras and portable devices. The scalability is achieved through a multi-level 2D dyadic wavelet decomposition featuring an accurate low-cost integer wavelet implementation with lifting. As our primary contribution, we present a modification to the SPECK wavelet coefficient encoding algorithm to significantly improve the efficiency of an embedded system implementation. The modification consists of storing the significance of all quadtree nodes in a buffer, where each node comprises several coefficients. This buffer is then used to efficiently construct the code with minimal and direct memory access. Our approach allows efficient parallel implementation on multi-core computer systems and gives a substantial reduction of memory access and thus power consumption. We report experimental results, showing an approximate gain factor of 1,000 in execution time compared to a straightforward SPECK implementation, when combined with code optimization on a common digital signal processor. This translates to 75 full color 4CIF 4:2:0 encoding cycles per second, clearly demonstrating the realtime capabilities of the proposed modification

Capture module for IP Elphel 353 cameras. Evaluation of performance

This project's main objective is to evaluate the performance of the capture made by Elphel 353 camera. The assessment will serve as a starting point for the integration of these cameras in the new Smart room built in the Department of Signal Theory and Communications ,UPC (Universitat Politècnica de Catalunya). First the most important properties of the camera and the tools provided by the camera are described, Next, we study how to use these tools to get images in two capture modes, online and offline. Once we know how to get images, we define the methods used for evaluation, finally the capture is evaluated and the results and conclusions are presented

End to end Multi-Objective Optimisation of H.264 and HEVC Codecs

All multimedia devices now incorporate video CODECs that comply with international video coding standards such as H.264 / MPEG4-AVC and the new High Efficiency Video Coding Standard (HEVC) otherwise known as H.265. Although the standard CODECs have been designed to include algorithms with optimal efficiency, large number of coding parameters can be used to fine tune their operation, within known constraints of for e.g., available computational power, bandwidth, consumer QoS requirements, etc. With large number of such parameters involved, determining which parameters will play a significant role in providing optimal quality of service within given constraints is a further challenge that needs to be met. Further how to select the values of the significant parameters so that the CODEC performs optimally under the given constraints is a further important question to be answered. This thesis proposes a framework that uses machine learning algorithms to model the performance of a video CODEC based on the significant coding parameters. Means of modelling both the Encoder and Decoder performance is proposed. We define objective functions that can be used to model the performance related properties of a CODEC, i.e., video quality, bit-rate and CPU time. We show that these objective functions can be practically utilised in video Encoder/Decoder designs, in particular in their performance optimisation within given operational and practical constraints. A Multi-objective Optimisation framework based on Genetic Algorithms is thus proposed to optimise the performance of a video codec. The framework is designed to jointly minimize the CPU Time, Bit-rate and to maximize the quality of the compressed video stream. The thesis presents the use of this framework in the performance modelling and multi-objective optimisation of the most widely used video coding standard in practice at present, H.264 and the latest video coding standard, H.265/HEVC. When a communication network is used to transmit video, performance related parameters of the communication channel will impact the end-to-end performance of the video CODEC. Network delays and packet loss will impact the quality of the video that is received at the decoder via the communication channel, i.e., even if a video CODEC is optimally configured network conditions will make the experience sub-optimal. Given the above the thesis proposes a design, integration and testing of a novel approach to simulating a wired network and the use of UDP protocol for the transmission of video data. This network is subsequently used to simulate the impact of packet loss and network delays on optimally coded video based on the framework previously proposed for the modelling and optimisation of video CODECs. The quality of received video under different levels of packet loss and network delay is simulated, concluding the impact on transmitted video based on their content and features

Audio Coding Based on Integer Transforms

Die Audiocodierung hat sich in den letzten Jahren zu einem sehr populären Forschungs- und Anwendungsgebiet entwickelt. Insbesondere gehörangepasste Verfahren zur Audiocodierung, wie etwa MPEG-1 Layer-3 (MP3) oder MPEG-2 Advanced Audio Coding (AAC), werden häufig zur effizienten Speicherung und Übertragung von Audiosignalen verwendet. Für professionelle Anwendungen, wie etwa die Archivierung und Übertragung im Studiobereich, ist hingegen eher eine verlustlose Audiocodierung angebracht. Die bisherigen Ansätze für gehörangepasste und verlustlose Audiocodierung sind technisch völlig verschieden. Moderne gehörangepasste Audiocoder basieren meist auf Filterbänken, wie etwa der überlappenden orthogonalen Transformation "Modifizierte Diskrete Cosinus-Transformation" (MDCT). Verlustlose Audiocoder hingegen verwenden meist prädiktive Codierung zur Redundanzreduktion. Nur wenige Ansätze zur transformationsbasierten verlustlosen Audiocodierung wurden bisher versucht. Diese Arbeit präsentiert einen neuen Ansatz hierzu, der das Lifting-Schema auf die in der gehörangepassten Audiocodierung verwendeten überlappenden Transformationen anwendet. Dies ermöglicht eine invertierbare Integer-Approximation der ursprünglichen Transformation, z.B. die IntMDCT als Integer-Approximation der MDCT. Die selbe Technik kann auch für Filterbänke mit niedriger Systemverzögerung angewandt werden. Weiterhin ermöglichen ein neuer, mehrdimensionaler Lifting-Ansatz und eine Technik zur Spektralformung von Quantisierungsfehlern eine Verbesserung der Approximation der ursprünglichen Transformation. Basierend auf diesen neuen Integer-Transformationen werden in dieser Arbeit neue Verfahren zur Audiocodierung vorgestellt. Die Verfahren umfassen verlustlose Audiocodierung, eine skalierbare verlustlose Erweiterung eines gehörangepassten Audiocoders und einen integrierten Ansatz zur fein skalierbaren gehörangepassten und verlustlosen Audiocodierung. Schließlich wird mit Hilfe der Integer-Transformationen ein neuer Ansatz zur unhörbaren Einbettung von Daten mit hohen Datenraten in unkomprimierte Audiosignale vorgestellt.In recent years audio coding has become a very popular field for research and applications. Especially perceptual audio coding schemes, such as MPEG-1 Layer-3 (MP3) and MPEG-2 Advanced Audio Coding (AAC), are widely used for efficient storage and transmission of music signals. Nevertheless, for professional applications, such as archiving and transmission in studio environments, lossless audio coding schemes are considered more appropriate. Traditionally, the technical approaches used in perceptual and lossless audio coding have been separate worlds. In perceptual audio coding, the use of filter banks, such as the lapped orthogonal transform "Modified Discrete Cosine Transform" (MDCT), has been the approach of choice being used by many state of the art coding schemes. On the other hand, lossless audio coding schemes mostly employ predictive coding of waveforms to remove redundancy. Only few attempts have been made so far to use transform coding for the purpose of lossless audio coding. This work presents a new approach of applying the lifting scheme to lapped transforms used in perceptual audio coding. This allows for an invertible integer-to-integer approximation of the original transform, e.g. the IntMDCT as an integer approximation of the MDCT. The same technique can also be applied to low-delay filter banks. A generalized, multi-dimensional lifting approach and a noise-shaping technique are introduced, allowing to further optimize the accuracy of the approximation to the original transform. Based on these new integer transforms, this work presents new audio coding schemes and applications. The audio coding applications cover lossless audio coding, scalable lossless enhancement of a perceptual audio coder and fine-grain scalable perceptual and lossless audio coding. Finally an approach to data hiding with high data rates in uncompressed audio signals based on integer transforms is described

Audio Coding Based on Integer Transforms

Die Audiocodierung hat sich in den letzten Jahren zu einem sehr populären Forschungs- und Anwendungsgebiet entwickelt. Insbesondere gehörangepasste Verfahren zur Audiocodierung, wie etwa MPEG-1 Layer-3 (MP3) oder MPEG-2 Advanced Audio Coding (AAC), werden häufig zur effizienten Speicherung und Übertragung von Audiosignalen verwendet. Für professionelle Anwendungen, wie etwa die Archivierung und Übertragung im Studiobereich, ist hingegen eher eine verlustlose Audiocodierung angebracht. Die bisherigen Ansätze für gehörangepasste und verlustlose Audiocodierung sind technisch völlig verschieden. Moderne gehörangepasste Audiocoder basieren meist auf Filterbänken, wie etwa der überlappenden orthogonalen Transformation "Modifizierte Diskrete Cosinus-Transformation" (MDCT). Verlustlose Audiocoder hingegen verwenden meist prädiktive Codierung zur Redundanzreduktion. Nur wenige Ansätze zur transformationsbasierten verlustlosen Audiocodierung wurden bisher versucht. Diese Arbeit präsentiert einen neuen Ansatz hierzu, der das Lifting-Schema auf die in der gehörangepassten Audiocodierung verwendeten überlappenden Transformationen anwendet. Dies ermöglicht eine invertierbare Integer-Approximation der ursprünglichen Transformation, z.B. die IntMDCT als Integer-Approximation der MDCT. Die selbe Technik kann auch für Filterbänke mit niedriger Systemverzögerung angewandt werden. Weiterhin ermöglichen ein neuer, mehrdimensionaler Lifting-Ansatz und eine Technik zur Spektralformung von Quantisierungsfehlern eine Verbesserung der Approximation der ursprünglichen Transformation. Basierend auf diesen neuen Integer-Transformationen werden in dieser Arbeit neue Verfahren zur Audiocodierung vorgestellt. Die Verfahren umfassen verlustlose Audiocodierung, eine skalierbare verlustlose Erweiterung eines gehörangepassten Audiocoders und einen integrierten Ansatz zur fein skalierbaren gehörangepassten und verlustlosen Audiocodierung. Schließlich wird mit Hilfe der Integer-Transformationen ein neuer Ansatz zur unhörbaren Einbettung von Daten mit hohen Datenraten in unkomprimierte Audiosignale vorgestellt.In recent years audio coding has become a very popular field for research and applications. Especially perceptual audio coding schemes, such as MPEG-1 Layer-3 (MP3) and MPEG-2 Advanced Audio Coding (AAC), are widely used for efficient storage and transmission of music signals. Nevertheless, for professional applications, such as archiving and transmission in studio environments, lossless audio coding schemes are considered more appropriate. Traditionally, the technical approaches used in perceptual and lossless audio coding have been separate worlds. In perceptual audio coding, the use of filter banks, such as the lapped orthogonal transform "Modified Discrete Cosine Transform" (MDCT), has been the approach of choice being used by many state of the art coding schemes. On the other hand, lossless audio coding schemes mostly employ predictive coding of waveforms to remove redundancy. Only few attempts have been made so far to use transform coding for the purpose of lossless audio coding. This work presents a new approach of applying the lifting scheme to lapped transforms used in perceptual audio coding. This allows for an invertible integer-to-integer approximation of the original transform, e.g. the IntMDCT as an integer approximation of the MDCT. The same technique can also be applied to low-delay filter banks. A generalized, multi-dimensional lifting approach and a noise-shaping technique are introduced, allowing to further optimize the accuracy of the approximation to the original transform. Based on these new integer transforms, this work presents new audio coding schemes and applications. The audio coding applications cover lossless audio coding, scalable lossless enhancement of a perceptual audio coder and fine-grain scalable perceptual and lossless audio coding. Finally an approach to data hiding with high data rates in uncompressed audio signals based on integer transforms is described

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Energy efficient enabling technologies for semantic video processing on mobile devices

Semantic object-based processing will play an increasingly important role in future multimedia systems due to the ubiquity of digital multimedia capture/playback technologies and increasing storage capacity. Although the object based paradigm has many undeniable benefits, numerous technical challenges remain before the applications becomes pervasive, particularly on computational constrained mobile devices. A fundamental issue is the ill-posed problem of semantic object segmentation. Furthermore, on battery powered mobile computing devices, the additional algorithmic complexity of semantic object based processing compared to conventional video processing is highly undesirable both from a real-time operation and battery life perspective. This thesis attempts to tackle these issues by firstly constraining the solution space and focusing on the human face as a primary semantic concept of use to users of mobile devices. A novel face detection algorithm is proposed, which from the outset was designed to be amenable to be offloaded from the host microprocessor to dedicated hardware, thereby providing real-time performance and reducing power consumption. The algorithm uses an Artificial Neural Network (ANN), whose topology and weights are evolved via a genetic algorithm (GA). The computational burden of the ANN evaluation is offloaded to a dedicated hardware accelerator, which is capable of processing any evolved network topology. Efficient arithmetic circuitry, which leverages modified Booth recoding, column compressors and carry save adders, is adopted throughout the design. To tackle the increased computational costs associated with object tracking or object based shape encoding, a novel energy efficient binary motion estimation architecture is proposed. Energy is reduced in the proposed motion estimation architecture by minimising the redundant operations inherent in the binary data. Both architectures are shown to compare favourable with the relevant prior art

An Applied Approach to Attacking Effectiveness in Professional Football

With ever growing financial invest in elite (association) football (soccer), it becomes increasingly more important to operate at the highest possible standard. A stable football organisation allows for more long-term success and greater investments. With this, objective and thorough analyses of the work done and future requirements become irreplaceable. This in turn opens the door for the instalment or development of analysis departments and analyst positions within elite football teams. These analysts, usually coming from a scientific background, rely on accurate and reliable data. The quality of the analyses, or analysts for that matter, then depends on the work done and choices made. The current research programme was designed to guide practitioners to effectively analyse and apply the data currently collected in elite football. Because match analysis in football has come a long way since first mentioned in a scientific journal in 1968. From basic notational metrics collected live during matches, through comprehensively studying video footage and having computers take over the collection of match parameters altogether. Currently, no matter whether positional time-motion data, metrics describing all on-the-ball match events or computerised combinations of the two are taken into consideration, the possibilities are seemingly endless. That means, analysts should be aware of the context of the data in order to appropriately interpret the results. Generally speaking, the focus and goals of the analyses should be clear and that is where this research programme started off. In many elite sports, like football, being successful depends on winning. Whether this is a short-term requirement or is treated as a long-term process with potential setbacks anticipated, is a decision made by clubs and directors. On the field, where the coach is responsible and analysts often support decision making, success often depends on scoring goals. Regardless of the fact one wants to score more or concede less than the opponent, at least one goal is required to win. That is why the current research programme acknowledged a goal to be the most decisive event in elite football. With that notion in mind, multiple studies were designed to answer the question of how teams can become more effective in scoring goals. The first study was designed to determine the differences between shots that led to a goal and shots that did not. This was done by studying attempts made from statistically identical locations on the pitch, however with changing situational circumstances (context). It was found that the magnitude of effect of opponent positioning changed for different shooting locations and offensive players. The results showed that the selected contextual factors were not able to explain all variance in shooting outcome, however that they should be kept in mind whilst analysing match events. Generally, since opponent presence and pressure were found to influence some shots, the notion that time (e.g. space) is of importance during an attacking sequence came to light. How and when this space between opposing players came to be, was studied in the second investigation of the current programme. Since (theoretically) balance exists between both teams of eleven players, a perturbation of this balance needs to occur prior to a goal scoring opportunity. Expert observers qualitatively analysed a series of match events between elite teams from ball recovery to the moment of the shot. Specific playing styles or actions were not identified to disturb a team's balance; however, agreement was found for a timepoint approximately five seconds prior to the shot to be most detrimental. Furthermore, experts showed stronger agreements when a sequence led to a goal, although they were unaware of the outcome. This importance of a strong perturbation, highlighted, again, the significance of sufficient space for an attacker to release a threatening shot. Finally, also since the balance was disturbed some time before the shot, there seems to be more at hand during a successful attack than a simply well-executed shot. After validating a low-cost tracking system in the third study, so this could be used in a sub-elite environment with a team willing to share full time-motion and tactical data, the final, fourth, study was designed. Here, physical parameters prior to shooting attempts were compared for different outcomes. It was found that an increased physical output, describing both the covered distance and intensity of movements, and fewer defenders behind the ball related to greater attacking success. Similarly, when defenders had to cover more distance prior to a shot, the chances of conceding a goal increased too. Together with a noteworthy relationship between lower match output and both attacking as well as defensive success, the importance of creating space or covering opponents were highlighted once more. Conclusively, by taking football's most decisive event, a goal, into account, the understanding of how to play more effectively has considerably grown. At the same time, the current research programme showed how analysts could effectively apply commonly collected match data into day-to-day practice. By identifying and acknowledging the context during crucial events, more information towards improved decision making and player profiling may be gathered. Future scientific studies may take this applied approach as a guideline for worthwhile large-scale solutions. With machines capable of learning along the way and taking comprehensive datasets into account, the potential for match analysts is substantial. With science supporting practitioners to answer the questions asked by coached and directors, financial investments in both fields may follow and allow for a bright future