Data-driven visual quality estimation using machine learning

Heutzutage werden viele visuelle Inhalte erstellt und sind zugänglich, was auf Verbesserungen der Technologie wie Smartphones und das Internet zurückzuführen ist. Es ist daher notwendig, die von den Nutzern wahrgenommene Qualität zu bewerten, um das Erlebnis weiter zu verbessern. Allerdings sind nur wenige der aktuellen Qualitätsmodelle speziell für höhere Auflösungen konzipiert, sagen mehr als nur den Mean Opinion Score vorher oder nutzen maschinelles Lernen. Ein Ziel dieser Arbeit ist es, solche maschinellen Modelle für höhere Auflösungen mit verschiedenen Datensätzen zu trainieren und zu evaluieren. Als Erstes wird eine objektive Analyse der Bildqualität bei höheren Auflösungen durchgeführt. Die Bilder wurden mit Video-Encodern komprimiert, hierbei weist AV1 die beste Qualität und Kompression auf. Anschließend werden die Ergebnisse eines Crowd-Sourcing-Tests mit einem Labortest bezüglich Bildqualität verglichen. Weiterhin werden auf Deep Learning basierende Modelle für die Vorhersage von Bild- und Videoqualität beschrieben. Das auf Deep Learning basierende Modell ist aufgrund der benötigten Ressourcen für die Vorhersage der Videoqualität in der Praxis nicht anwendbar. Aus diesem Grund werden pixelbasierte Videoqualitätsmodelle vorgeschlagen und ausgewertet, die aussagekräftige Features verwenden, welche Bild- und Bewegungsaspekte abdecken. Diese Modelle können zur Vorhersage von Mean Opinion Scores für Videos oder sogar für anderer Werte im Zusammenhang mit der Videoqualität verwendet werden, wie z.B. einer Bewertungsverteilung. Die vorgestellte Modellarchitektur kann auf andere Videoprobleme angewandt werden, wie z.B. Videoklassifizierung, Vorhersage der Qualität von Spielevideos, Klassifikation von Spielegenres oder der Klassifikation von Kodierungsparametern. Ein wichtiger Aspekt ist auch die Verarbeitungszeit solcher Modelle. Daher wird ein allgemeiner Ansatz zur Beschleunigung von State-of-the-Art-Videoqualitätsmodellen vorgestellt, der zeigt, dass ein erheblicher Teil der Verarbeitungszeit eingespart werden kann, während eine ähnliche Vorhersagegenauigkeit erhalten bleibt. Die Modelle sind als Open Source veröffentlicht, so dass die entwickelten Frameworks für weitere Forschungsarbeiten genutzt werden können. Außerdem können die vorgestellten Ansätze als Bausteine für neuere Medienformate verwendet werden.Today a lot of visual content is accessible and produced, due to improvements in technology such as smartphones and the internet. This results in a need to assess the quality perceived by users to further improve the experience. However, only a few of the state-of-the-art quality models are specifically designed for higher resolutions, predict more than mean opinion score, or use machine learning. One goal of the thesis is to train and evaluate such machine learning models of higher resolutions with several datasets. At first, an objective evaluation of image quality in case of higher resolutions is performed. The images are compressed using video encoders, and it is shown that AV1 is best considering quality and compression. This evaluation is followed by the analysis of a crowdsourcing test in comparison with a lab test investigating image quality. Afterward, deep learning-based models for image quality prediction and an extension for video quality are proposed. However, the deep learning-based video quality model is not practically usable because of performance constrains. For this reason, pixel-based video quality models using well-motivated features covering image and motion aspects are proposed and evaluated. These models can be used to predict mean opinion scores for videos, or even to predict other video quality-related information, such as a rating distributions. The introduced model architecture can be applied to other video problems, such as video classification, gaming video quality prediction, gaming genre classification or encoding parameter estimation. Furthermore, one important aspect is the processing time of such models. Hence, a generic approach to speed up state-of-the-art video quality models is introduced, which shows that a significant amount of processing time can be saved, while achieving similar prediction accuracy. The models have been made publicly available as open source so that the developed frameworks can be used for further research. Moreover, the presented approaches may be usable as building blocks for newer media formats

Perceptual video quality assessment: the journey continues!

Perceptual Video Quality Assessment (VQA) is one of the most fundamental and challenging problems in the field of Video Engineering. Along with video compression, it has become one of two dominant theoretical and algorithmic technologies in television streaming and social media. Over the last 2 decades, the volume of video traffic over the internet has grown exponentially, powered by rapid advancements in cloud services, faster video compression technologies, and increased access to high-speed, low-latency wireless internet connectivity. This has given rise to issues related to delivering extraordinary volumes of picture and video data to an increasingly sophisticated and demanding global audience. Consequently, developing algorithms to measure the quality of pictures and videos as perceived by humans has become increasingly critical since these algorithms can be used to perceptually optimize trade-offs between quality and bandwidth consumption. VQA models have evolved from algorithms developed for generic 2D videos to specialized algorithms explicitly designed for on-demand video streaming, user-generated content (UGC), virtual and augmented reality (VR and AR), cloud gaming, high dynamic range (HDR), and high frame rate (HFR) scenarios. Along the way, we also describe the advancement in algorithm design, beginning with traditional hand-crafted feature-based methods and finishing with current deep-learning models powering accurate VQA algorithms. We also discuss the evolution of Subjective Video Quality databases containing videos and human-annotated quality scores, which are the necessary tools to create, test, compare, and benchmark VQA algorithms. To finish, we discuss emerging trends in VQA algorithm design and general perspectives on the evolution of Video Quality Assessment in the foreseeable future

Perceptual video quality and quality of experience for adaptive video streaming

We live in a world where images and videos dominate our everyday lives. Every day, an enormous amount of video data is being shared in social media and consumer applications, while video streaming is becoming a new form of digital entertainment. Large-scale video streaming on demand has become possible thanks to numerous engineering achievements in fields such as video compression, high-speed computation and display technologies. Nevertheless, the skyrocketing needs for bandwidth and network resources consumed by video applications challenges modern video content delivery. Since the available bandwidth resources are limited, streaming service providers have to mediate between operation costs, bandwidth efficiency and maximizing user quality of experience. However, these goals are inherently conflicting and require knowledge of how user quality of experience is affected by the network-induced changes in video quality. Being able to understand and predict user quality of experience and perceptually optimize rate allocation, can have significant effects in better network utilization, reduced costs for service providers and improved user satisfaction. The goal of this dissertation is to study and predict user quality of experience in video streaming applications, by exploiting perceptual video quality and human behavioral responses to streaming-related video impairments. To this end, I present the details of three large-scale video subjective studies which target video streaming under multiple viewing conditions, such as display device, session duration, content characteristics and network/buffer conditions. By analyzing how humans react to changes in visual quality and streaming video impairments, I also design numerous video quality and quality of experience prediction models that can be used to evaluate the overall and the continuous-time perceived video quality. Throughout this dissertation, my goal is to perceptually optimize various stages of the video streaming pipeline, such as video encoding and video quality control as well as client-based rate adaptation. Ultimately, I envision that the outcome of this dissertation can be useful for video streaming applications at global scaleElectrical and Computer Engineerin

Optimisation énergétique de processus de traitement du signal et ses applications au décodage vidéo

Consumer electronics offer today more and more features (video, audio, GPS, Internet) and connectivity means (multi-radio systems with WiFi, Bluetooth, UMTS, HSPA, LTE-advanced ... ). The power demand of these devices is growing for the digital part especially for the processing chip. To support this ever increasing computing demand, processor architectures have evolved with multicore processors, graphics processors (GPU) and ether dedicated hardware accelerators. However, the evolution of battery technology is itself slower. Therefore, the autonomy of embedded systems is now under a great pressure. Among the new functionalities supported by mobile devices, video services take a prominent place. lndeed, recent analyzes show that they will represent 70% of mobile Internet traffic by 2016. Accompanying this growth, new technologies are emerging for new services and applications. Among them HEVC (High Efficiency Video Coding) can double the data compression while maintaining a subjective quality equivalent to its predecessor, the H.264 standard. ln a digital circuit, the total power consumption is made of static power and dynamic power. Most of modern hardware architectures implement means to control the power consumption of the system. Dynamic Voltage and Frequency Scaling (DVFS) mainly reduces the dynamic power of the circuit. This technique aims to adapt the power of the processor (and therefore its consumption) to the actual load needed by the application. To control the static power, Dynamic Power Management (DPM or sleep modes) aims to stop the voltage supplies associated with specific areas of the chip. ln this thesis, we first present a model of the energy consumed by the circuit integrating DPM and DVFS modes. This model is generalized to multi-core integrated circuits and to a rapid prototyping tool. Thus, the optimal operating point of a circuit, i.e. the operating frequency and the number of active cores, is identified. Secondly, the HEVC application is integrated to a multicore architecture coupled with a sophisticated DVFS mechanism. We show that this application can be implemented efficiently on general purpose processors (GPP) while minimizing the power consumption. Finally, and to get further energy gain, we propose a modified HEVC decoder that is capable to tune its energy gains together with a decoding quality trade-off.Aujourd'hui, les appareils électroniques offrent de plus en plus de fonctionnalités (vidéo, audio, GPS, internet) et des connectivités variées (multi-systèmes de radio avec WiFi, Bluetooth, UMTS, HSPA, LTE-advanced ... ). La demande en puissance de ces appareils est donc grandissante pour la partie numérique et notamment le processeur de calcul. Pour répondre à ce besoin sans cesse croissant de nouvelles fonctionnalités et donc de puissance de calcul, les architectures des processeurs ont beaucoup évolué : processeurs multi-coeurs, processeurs graphiques (GPU) et autres accélérateurs matériels dédiés. Cependant, alors que de nouvelles architectures matérielles peinent à répondre aux exigences de performance, l'évolution de la technologie des batteries est quant à elle encore plus lente. En conséquence, l'autonomie des systèmes embarqués est aujourd'hui sous pression. Parmi les nouveaux services supportés par les terminaux mobiles, la vidéo prend une place prépondérante. En effet, des analyses récentes de tendance montrent qu'elle représentera 70 % du trafic internet mobile dès 2016. Accompagnant cette croissance, de nouvelles technologies émergent permettant de nouveaux services et applications. Parmi elles, HEVC (High Efficiency Video Coding) permet de doubler la compression de données tout en garantissant une qualité subjective équivalente à son prédécesseur, la norme H.264. Dans un circuit numérique, la consommation provient de deux éléments: la puissance statique et la puissance dynamique. La plupart des architectures matérielles récentes mettent en oeuvre des procédés permettant de contrôler la puissance du système. Le changement dynamique du couple tension/fréquence appelé Dynamic Voltage and Frequency Scaling (DVFS) agit principalement sur la puissance dynamique du circuit. Cette technique permet d'adapter la puissance du processeur (et donc sa consommation) à la charge réelle nécessaire pour une application. Pour contrôler la puissance statique, le Dynamic Power Management (DPM, ou modes de veille) consistant à arrêter les alimentations associées à des zones spécifiques de la puce. Dans cette thèse, nous présentons d'abord une modélisation de l'énergie consommée par le circuit intégrant les modes DVFS et DPM. Cette modélisation est généralisée au circuit multi-coeurs et intégrée à un outil de prototypage rapide. Ainsi le point de fonctionnement optimal d'un circuit, la fréquence de fonctionnement et le nombre de coeurs actifs, est identifié. Dans un second temps, l'application HEVC est intégrée à une architecture multi-coeurs avec une adaptation dynamique de la fréquence de développement. Nous montrons que cette application peut être implémentée efficacement sur des processeurs généralistes (GPP) tout en minimisant la puissance consommée. Enfin, et pour aller plus loin dans les gains en énergie, nous proposons une modification du décodeur HEVC qui permet à un décodeur de baisser encore plus sa consommation en fonction du budget énergétique disponible localement

Geo-Tagged Video Management: Storage, Queries and Streaming

Ph.DDOCTOR OF PHILOSOPH

Game Theory Relaunched

The game is on. Do you know how to play? Game theory sets out to explore what can be said about making decisions which go beyond accepting the rules of a game. Since 1942, a well elaborated mathematical apparatus has been developed to do so; but there is more. During the last three decades game theoretic reasoning has popped up in many other fields as well - from engineering to biology and psychology. New simulation tools and network analysis have made game theory omnipresent these days. This book collects recent research papers in game theory, which come from diverse scientific communities all across the world; they combine many different fields like economics, politics, history, engineering, mathematics, physics, and psychology. All of them have as a common denominator some method of game theory. Enjoy

5G Outlook – Innovations and Applications

5G Outlook - Innovations and Applications is a collection of the recent research and development in the area of the Fifth Generation Mobile Technology (5G), the future of wireless communications. Plenty of novel ideas and knowledge of the 5G are presented in this book as well as divers applications from health science to business modeling. The authors of different chapters contributed from various countries and organizations. The chapters have also been presented at the 5th IEEE 5G Summit held in Aalborg on July 1, 2016. The book starts with a comprehensive introduction on 5G and its need and requirement. Then millimeter waves as a promising spectrum to 5G technology is discussed. The book continues with the novel and inspiring ideas for the future wireless communication usage and network. Further, some technical issues in signal processing and network design for 5G are presented. Finally, the book ends up with different applications of 5G in distinct areas. Topics widely covered in this book are: • 5G technology from past to present to the future• Millimeter- waves and their characteristics• Signal processing and network design issues for 5G• Applications, business modeling and several novel ideas for the future of 5