3D multiple description coding for error resilience over wireless networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Mobile communications has gained a growing interest from both customers and service providers alike in the last 1-2 decades. Visual information is used in many application domains such as remote health care, video –on demand, broadcasting, video surveillance etc. In order to enhance the visual effects of digital video content, the depth perception needs to be provided with the actual visual content. 3D video has earned a significant interest from the research community in recent years, due to the tremendous impact it leaves on viewers and its enhancement of the user’s quality of experience (QoE). In the near future, 3D video is likely to be used in most video applications, as it offers a greater sense of immersion and perceptual experience. When 3D video is compressed and transmitted over error prone channels, the associated packet loss leads to visual quality degradation. When a picture is lost or corrupted so severely that the concealment result is not acceptable, the receiver typically pauses video playback and waits for the next INTRA picture to resume decoding. Error propagation caused by employing predictive coding may degrade the video quality severely. There are several ways used to mitigate the effects of such transmission errors. One widely used technique in International Video Coding Standards is error resilience. The motivation behind this research work is that, existing schemes for 2D colour video compression such as MPEG, JPEG and H.263 cannot be applied to 3D video content. 3D video signals contain depth as well as colour information and are bandwidth demanding, as they require the transmission of multiple high-bandwidth 3D video streams. On the other hand, the capacity of wireless channels is limited and wireless links are prone to various types of errors caused by noise, interference, fading, handoff, error burst and network congestion. Given the maximum bit rate budget to represent the 3D scene, optimal bit-rate allocation between texture and depth information rendering distortion/losses should be minimised. To mitigate the effect of these errors on the perceptual 3D video quality, error resilience video coding needs to be investigated further to offer better quality of experience (QoE) to end users. This research work aims at enhancing the error resilience capability of compressed 3D video, when transmitted over mobile channels, using Multiple Description Coding (MDC) in order to improve better user’s quality of experience (QoE). Furthermore, this thesis examines the sensitivity of the human visual system (HVS) when employed to view 3D video scenes. The approach used in this study is to use subjective testing in order to rate people’s perception of 3D video under error free and error prone conditions through the use of a carefully designed bespoke questionnaire.Petroleum Technology Development Fund (PTDF

BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation

Event cameras provide high temporal precision, low data rates, and high dynamic range visual perception, which are well-suited for optical flow estimation. While data-driven optical flow estimation has obtained great success in RGB cameras, its generalization performance is seriously hindered in event cameras mainly due to the limited and biased training data. In this paper, we present a novel simulator, BlinkSim, for the fast generation of large-scale data for event-based optical flow. BlinkSim consists of a configurable rendering engine and a flexible engine for event data simulation. By leveraging the wealth of current 3D assets, the rendering engine enables us to automatically build up thousands of scenes with different objects, textures, and motion patterns and render very high-frequency images for realistic event data simulation. Based on BlinkSim, we construct a large training dataset and evaluation benchmark BlinkFlow that contains sufficient, diversiform, and challenging event data with optical flow ground truth. Experiments show that BlinkFlow improves the generalization performance of state-of-the-art methods by more than 40% on average and up to 90%. Moreover, we further propose an Event optical Flow transFormer (E-FlowFormer) architecture. Powered by our BlinkFlow, E-FlowFormer outperforms the SOTA methods by up to 91% on MVSEC dataset and 14% on DSEC dataset and presents the best generalization performance

3D multiple description coding for error resilience over wireless networks

Mobile communications has gained a growing interest from both customers and service providers alike in the last 1-2 decades. Visual information is used in many application domains such as remote health care, video –on demand, broadcasting, video surveillance etc. In order to enhance the visual effects of digital video content, the depth perception needs to be provided with the actual visual content. 3D video has earned a significant interest from the research community in recent years, due to the tremendous impact it leaves on viewers and its enhancement of the user’s quality of experience (QoE). In the near future, 3D video is likely to be used in most video applications, as it offers a greater sense of immersion and perceptual experience. When 3D video is compressed and transmitted over error prone channels, the associated packet loss leads to visual quality degradation. When a picture is lost or corrupted so severely that the concealment result is not acceptable, the receiver typically pauses video playback and waits for the next INTRA picture to resume decoding. Error propagation caused by employing predictive coding may degrade the video quality severely. There are several ways used to mitigate the effects of such transmission errors. One widely used technique in International Video Coding Standards is error resilience. The motivation behind this research work is that, existing schemes for 2D colour video compression such as MPEG, JPEG and H.263 cannot be applied to 3D video content. 3D video signals contain depth as well as colour information and are bandwidth demanding, as they require the transmission of multiple high-bandwidth 3D video streams. On the other hand, the capacity of wireless channels is limited and wireless links are prone to various types of errors caused by noise, interference, fading, handoff, error burst and network congestion. Given the maximum bit rate budget to represent the 3D scene, optimal bit-rate allocation between texture and depth information rendering distortion/losses should be minimised. To mitigate the effect of these errors on the perceptual 3D video quality, error resilience video coding needs to be investigated further to offer better quality of experience (QoE) to end users. This research work aims at enhancing the error resilience capability of compressed 3D video, when transmitted over mobile channels, using Multiple Description Coding (MDC) in order to improve better user’s quality of experience (QoE). Furthermore, this thesis examines the sensitivity of the human visual system (HVS) when employed to view 3D video scenes. The approach used in this study is to use subjective testing in order to rate people’s perception of 3D video under error free and error prone conditions through the use of a carefully designed bespoke questionnaire.EThOS - Electronic Theses Online ServicePetroleum Technology Development Fund (PTDF)GBUnited Kingdo

Super Resolution of Wavelet-Encoded Images and Videos

In this dissertation, we address the multiframe super resolution reconstruction problem for wavelet-encoded images and videos. The goal of multiframe super resolution is to obtain one or more high resolution images by fusing a sequence of degraded or aliased low resolution images of the same scene. Since the low resolution images may be unaligned, a registration step is required before super resolution reconstruction. Therefore, we first explore in-band (i.e. in the wavelet-domain) image registration; then, investigate super resolution. Our motivation for analyzing the image registration and super resolution problems in the wavelet domain is the growing trend in wavelet-encoded imaging, and wavelet-encoding for image/video compression. Due to drawbacks of widely used discrete cosine transform in image and video compression, a considerable amount of literature is devoted to wavelet-based methods. However, since wavelets are shift-variant, existing methods cannot utilize wavelet subbands efficiently. In order to overcome this drawback, we establish and explore the direct relationship between the subbands under a translational shift, for image registration and super resolution. We then employ our devised in-band methodology, in a motion compensated video compression framework, to demonstrate the effective usage of wavelet subbands. Super resolution can also be used as a post-processing step in video compression in order to decrease the size of the video files to be compressed, with downsampling added as a pre-processing step. Therefore, we present a video compression scheme that utilizes super resolution to reconstruct the high frequency information lost during downsampling. In addition, super resolution is a crucial post-processing step for satellite imagery, due to the fact that it is hard to update imaging devices after a satellite is launched. Thus, we also demonstrate the usage of our devised methods in enhancing resolution of pansharpened multispectral images

Construction de mosaïques de super-résolution à partir de la vidéo de basse résolution. Application au résumé vidéo et la dissimulation d'erreurs de transmission.

La numérisation des vidéos existantes ainsi que le développement explosif des services multimédia par des réseaux comme la diffusion de la télévision numérique ou les communications mobiles ont produit une énorme quantité de vidéos compressées. Ceci nécessite des outils d’indexation et de navigation efficaces, mais une indexation avant l’encodage n’est pas habituelle. L’approche courante est le décodage complet des ces vidéos pour ensuite créer des indexes. Ceci est très coûteux et par conséquent non réalisable en temps réel. De plus, des informations importantes comme le mouvement, perdus lors du décodage, sont reestimées bien que déjà présentes dans le flux comprimé. Notre but dans cette thèse est donc la réutilisation des données déjà présents dans le flux comprimé MPEG pour l’indexation et la navigation rapide. Plus précisément, nous extrayons des coefficients DC et des vecteurs de mouvement. Dans le cadre de cette thèse, nous nous sommes en particulier intéressés à la construction de mosaïques à partir des images DC extraites des images I. Une mosaïque est construite par recalage et fusion de toutes les images d’une séquence vidéo dans un seul système de coordonnées. Ce dernier est en général aligné avec une des images de la séquence : l’image de référence. Il en résulte une seule image qui donne une vue globale de la séquence. Ainsi, nous proposons dans cette thèse un système complet pour la construction des mosaïques à partir du flux MPEG-1/2 qui tient compte de différentes problèmes apparaissant dans des séquences vidéo réeles, comme par exemple des objets en mouvment ou des changements d’éclairage. Une tâche essentielle pour la construction d’une mosaïque est l’estimation de mouvement entre chaque image de la séquence et l’image de référence. Notre méthode se base sur une estimation robuste du mouvement global de la caméra à partir des vecteurs de mouvement des images P. Cependant, le mouvement global de la caméra estimé pour une image P peut être incorrect car il dépend fortement de la précision des vecteurs encodés. Nous détectons les images P concernées en tenant compte des coefficients DC de l’erreur encodée associée et proposons deux méthodes pour corriger ces mouvements. Unemosaïque construite à partir des images DC a une résolution très faible et souffre des effets d’aliasing dus à la nature des images DC. Afin d’augmenter sa résolution et d’améliorer sa qualité visuelle, nous appliquons une méthode de super-résolution basée sur des rétro-projections itératives. Les méthodes de super-résolution sont également basées sur le recalage et la fusion des images d’une séquence vidéo, mais sont accompagnées d’une restauration d’image. Dans ce cadre, nous avons développé une nouvelleméthode d’estimation de flou dû au mouvement de la caméra ainsi qu’une méthode correspondante de restauration spectrale. La restauration spectrale permet de traiter le flou globalement, mais, dans le cas des obvi jets ayant un mouvement indépendant du mouvement de la caméra, des flous locaux apparaissent. C’est pourquoi, nous proposons un nouvel algorithme de super-résolution dérivé de la restauration spatiale itérative de Van Cittert et Jansson permettant de restaurer des flous locaux. En nous basant sur une segmentation d’objets en mouvement, nous restaurons séparément lamosaïque d’arrière-plan et les objets de l’avant-plan. Nous avons adapté notre méthode d’estimation de flou en conséquence. Dans une premier temps, nous avons appliqué notre méthode à la construction de résumé vidéo avec pour l’objectif la navigation rapide par mosaïques dans la vidéo compressée. Puis, nous établissions comment la réutilisation des résultats intermédiaires sert à d’autres tâches d’indexation, notamment à la détection de changement de plan pour les images I et à la caractérisation dumouvement de la caméra. Enfin, nous avons exploré le domaine de la récupération des erreurs de transmission. Notre approche consiste en construire une mosaïque lors du décodage d’un plan ; en cas de perte de données, l’information manquante peut être dissimulée grace à cette mosaïque

Advanced Information Systems and Technologies

This book comprises the proceedings of the V International Scientific Conference "Advanced Information Systems and Technologies, AIST-2017". The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing computer networking and telecomunications. They will be useful for students, graduate students, researchers who interested in computer science

Adaptive video delivery using semantics

The diffusion of network appliances such as cellular phones, personal digital assistants and hand-held computers has created the need to personalize the way media content is delivered to the end user. Moreover, recent devices, such as digital radio receivers with graphics displays, and new applications, such as intelligent visual surveillance, require novel forms of video analysis for content adaptation and summarization. To cope with these challenges, we propose an automatic method for the extraction of semantics from video, and we present a framework that exploits these semantics in order to provide adaptive video delivery. First, an algorithm that relies on motion information to extract multiple semantic video objects is proposed. The algorithm operates in two stages. In the first stage, a statistical change detector produces the segmentation of moving objects from the background. This process is robust with regard to camera noise and does not need manual tuning along a sequence or for different sequences. In the second stage, feedbacks between an object partition and a region partition are used to track individual objects along the frames. These interactions allow us to cope with multiple, deformable objects, occlusions, splitting, appearance and disappearance of objects, and complex motion. Subsequently, semantics are used to prioritize visual data in order to improve the performance of adaptive video delivery. The idea behind this approach is to organize the content so that a particular network or device does not inhibit the main content message. Specifically, we propose two new video adaptation strategies. The first strategy combines semantic analysis with a traditional frame-based video encoder. Background simplifications resulting from this approach do not penalize overall quality at low bitrates. The second strategy uses metadata to efficiently encode the main content message. The metadata-based representation of object's shape and motion suffices to convey the meaning and action of a scene when the objects are familiar. The impact of different video adaptation strategies is then quantified with subjective experiments. We ask a panel of human observers to rate the quality of adapted video sequences on a normalized scale. From these results, we further derive an objective quality metric, the semantic peak signal-to-noise ratio (SPSNR), that accounts for different image areas and for their relevance to the observer in order to reflect the focus of attention of the human visual system. At last, we determine the adaptation strategy that provides maximum value for the end user by maximizing the SPSNR for given client resources at the time of delivery. By combining semantic video analysis and adaptive delivery, the solution presented in this dissertation permits the distribution of video in complex media environments and supports a large variety of content-based applications

Advanced Information Systems and Technologies

This book comprises the proceedings of the V International Scientific Conference "Advanced Information Systems and Technologies, AIST-2017". The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing computer networking and telecomunications. They will be useful for students, graduate students, researchers who interested in computer science

Robust and Efficient Camera-based Scene Reconstruction

For the simultaneous reconstruction of 3D scene geometry and camera poses from images or videos, there are two major approaches: On the one hand it is possible to perform a sparse reconstruction by extracting recognizable features from multiple images which correspond to the same 3D points in the scene. With those features, the positions of the 3D points as well as the camera poses can be obtained such that they explain the positions of the features in the images best. On the other hand, on video data, a dense reconstruction can be obtained by alternating between the tracking of the camera pose and updating a depth map representing the scene per frame of the video. In this dissertation, we introduce several improvements to both reconstruction strategies. We start from improving the reliability of image feature matches which leads to faster and more robust subsequent processing. Then, we present a sparse reconstruction pipeline completely optimized for high resolution and high frame rate video, exploiting the redundancy in the data to gain more efficiency. For (semi-)dense reconstruction on camera rigs which is prone to calibration inaccuracies, we show how to model and recover the rig calibration online in the reconstruction process. Finally, we explore the applicability of machine learning based on neural networks to the relative camera pose problem, focusing mainly on generating optimal training data. Robust and fast 3D reconstruction of the environment is demanded in several currently emerging applications ranging from set scanning for movies and computer games over inside-out tracking based augmented reality devices to autonomous robots and drones as well as self-driving cars.Für die gemeinsame Rekonstruktion von 3D Szenengeometrie und Kamera-Posen aus Bildern oder Videos gibt es zwei grundsätzliche Ansätze: Auf der einen Seite kann eine aus wenigen Oberflächen-Punkten bestehende Rekonstruktion erstellt werden, indem einzelne wiedererkennbare Features, die zum selben 3D-Punkt der Szene gehören, aus Bildern extrahiert werden. Mit diesen Features können die Position der 3D-Punkte sowie die Posen der Kameras so bestimmt werden, dass sie die Positionen der Features in den Bildern bestmöglich erklären. Auf der anderen Seite können bei Videos dichter gesampelte Oberflächen rekonstruiert werden, indem für jedes Einzelbild zuerst die Kamera-Pose bestimmt und dann die Szenengeometrie, die als Tiefenkarte vorhanden ist, verbessert wird. In dieser Dissertation werden verschiedene Verbesserungen für beide Rekonstruktionsstrategien vorgestellt. Wir beginnen damit, die Zuverlässigkeit beim Finden von Bildfeature-Paaren zu erhöhen, was zu einer robusteren und schnelleren Verarbeitung in den weiteren Rekonstruktionsschritten führt. Außerdem präsentieren wir eine Rekonstruktions-Pipeline für die Feature-basierte Rekonstruktion, die auf hohe Auflösungen und Bildwiederholraten optimiert ist und die Redundanz in entsprechenden Daten für eine effizientere Verarbeitung ausnutzt. Für die dichte Rekonstruktion von Oberflächen mit Multi-Kamera-Rigs, welche anfällig für Kalibrierungsungenauigkeiten ist, beschreiben wir, wie die Posen der Kameras innerhalb des Rigs modelliert und im Rekonstruktionsprozess laufend bestimmt werden können. Schließlich untersuchen wir die Anwendbarkeit von maschinellem Lernen basierend auf neuralen Netzen auf das Problem der Bestimmung der relativen Kamera-Pose. Unser Hauptaugenmerk liegt dabei auf dem Generieren möglichst optimaler Trainingsdaten. Eine robuste und schnelle 3D-Rekonstruktion der Umgebung wird in vielen zur Zeit aufstrebenden Anwendungsgebieten benötigt: Beim Erzeugen virtueller Abbilder realer Umgebungen für Filme und Computerspiele, bei inside-out Tracking basierten Augmented Reality Geräten, für autonome Roboter und Drohnen sowie bei selbstfahrenden Autos

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE