Compression and Subjective Quality Assessment of 3D Video

In recent years, three-dimensional television (3D TV) has been broadly considered as the successor to the existing traditional two-dimensional television (2D TV) sets. With its capability of offering a dynamic and immersive experience, 3D video (3DV) is expected to expand conventional video in several applications in the near future. However, 3D content requires more than a single view to deliver the depth sensation to the viewers and this, inevitably, increases the bitrate compared to the corresponding 2D content. This need drives the research trend in video compression field towards more advanced and more efficient algorithms. Currently, the Advanced Video Coding (H.264/AVC) is the state-of-the-art video coding standard which has been developed by the Joint Video Team of ISO/IEC MPEG and ITU-T VCEG. This codec has been widely adopted in various applications and products such as TV broadcasting, video conferencing, mobile TV, and blue-ray disc. One important extension of H.264/AVC, namely Multiview Video Coding (MVC) was an attempt to multiple view compression by taking into consideration the inter-view dependency between different views of the same scene. This codec H.264/AVC with its MVC extension (H.264/MVC) can be used for encoding either conventional stereoscopic video, including only two views, or multiview video, including more than two views. In spite of the high performance of H.264/MVC, a typical multiview video sequence requires a huge amount of storage space, which is proportional to the number of offered views. The available views are still limited and the research has been devoted to synthesizing an arbitrary number of views using the multiview video and depth map (MVD). This process is mandatory for auto-stereoscopic displays (ASDs) where many views are required at the viewer side and there is no way to transmit such a relatively huge number of views with currently available broadcasting technology. Therefore, to satisfy the growing hunger for 3D related applications, it is mandatory to further decrease the bitstream by introducing new and more efficient algorithms for compressing multiview video and depth maps. This thesis tackles the 3D content compression targeting different formats i.e. stereoscopic video and depth-enhanced multiview video. Stereoscopic video compression algorithms introduced in this thesis mostly focus on proposing different types of asymmetry between the left and right views. This means reducing the quality of one view compared to the other view aiming to achieve a better subjective quality against the symmetric case (the reference) and under the same bitrate constraint. The proposed algorithms to optimize depth-enhanced multiview video compression include both texture compression schemes as well as depth map coding tools. Some of the introduced coding schemes proposed for this format include asymmetric quality between the views. Knowing that objective metrics are not able to accurately estimate the subjective quality of stereoscopic content, it is suggested to perform subjective quality assessment to evaluate different codecs. Moreover, when the concept of asymmetry is introduced, the Human Visual System (HVS) performs a fusion process which is not completely understood. Therefore, another important aspect of this thesis is conducting several subjective tests and reporting the subjective ratings to evaluate the perceived quality of the proposed coded content against the references. Statistical analysis is carried out in the thesis to assess the validity of the subjective ratings and determine the best performing test cases

Perceptual modelling for 2D and 3D

Livrable D1.1 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D1.1 du projet

Enhanced Ultrasound Visualization for Procedure Guidance

Intra-cardiac procedures often involve fast-moving anatomic structures with large spatial extent and high geometrical complexity. Real-time visualization of the moving structures and instrument-tissue contact is crucial to the success of these procedures. Real-time 3D ultrasound is a promising modality for procedure guidance as it offers improved spatial orientation information relative to 2D ultrasound. Imaging rates at 30 fps enable good visualization of instrument-tissue interactions, far faster than the volumetric imaging alternatives (MR/CT). Unlike fluoroscopy, 3D ultrasound also allows better contrast of soft tissues, and avoids the use of ionizing radiation.Engineering and Applied Science

Perceptually Optimized Visualization on Autostereoscopic 3D Displays

The family of displays, which aims to visualize a 3D scene with realistic depth, are known as "3D displays". Due to technical limitations and design decisions, such displays create visible distortions, which are interpreted by the human vision as artefacts. In absence of visual reference (e.g. the original scene is not available for comparison) one can improve the perceived quality of the representations by making the distortions less visible. This thesis proposes a number of signal processing techniques for decreasing the visibility of artefacts on 3D displays. The visual perception of depth is discussed, and the properties (depth cues) of a scene which the brain uses for assessing an image in 3D are identified. Following the physiology of vision, a taxonomy of 3D artefacts is proposed. The taxonomy classifies the artefacts based on their origin and on the way they are interpreted by the human visual system. The principles of operation of the most popular types of 3D displays are explained. Based on the display operation principles, 3D displays are modelled as a signal processing channel. The model is used to explain the process of introducing distortions. It also allows one to identify which optical properties of a display are most relevant to the creation of artefacts. A set of optical properties for dual-view and multiview 3D displays are identified, and a methodology for measuring them is introduced. The measurement methodology allows one to derive the angular visibility and crosstalk of each display element without the need for precision measurement equipment. Based on the measurements, a methodology for creating a quality profile of 3D displays is proposed. The quality profile can be either simulated using the angular brightness function or directly measured from a series of photographs. A comparative study introducing the measurement results on the visual quality and position of the sweet-spots of eleven 3D displays of different types is presented. Knowing the sweet-spot position and the quality profile allows for easy comparison between 3D displays. The shape and size of the passband allows depth and textures of a 3D content to be optimized for a given 3D display. Based on knowledge of 3D artefact visibility and an understanding of distortions introduced by 3D displays, a number of signal processing techniques for artefact mitigation are created. A methodology for creating anti-aliasing filters for 3D displays is proposed. For multiview displays, the methodology is extended towards so-called passband optimization which addresses Moiré, fixed-pattern-noise and ghosting artefacts, which are characteristic for such displays. Additionally, design of tuneable anti-aliasing filters is presented, along with a framework which allows the user to select the so-called 3d sharpness parameter according to his or her preferences. Finally, a set of real-time algorithms for view-point-based optimization are presented. These algorithms require active user-tracking, which is implemented as a combination of face and eye-tracking. Once the observer position is known, the image on a stereoscopic display is optimised for the derived observation angle and distance. For multiview displays, the combination of precise light re-direction and less-precise face-tracking is used for extending the head parallax. For some user-tracking algorithms, implementation details are given, regarding execution of the algorithm on a mobile device or on desktop computer with graphical accelerator

A Modular and Open-Source Framework for Virtual Reality Visualisation and Interaction in Bioimaging

Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data from analysis of such data or simulations. The advent of new imaging technologies, such as lightsheet microscopy, has resulted in the users being confronted with an ever-growing amount of data, with even terabytes of imaging data created within a day. With the possibility of gentler and more high-performance imaging, the spatiotemporal complexity of the model systems or processes of interest is increasing as well. Visualisation is often the first step in making sense of this data, and a crucial part of building and debugging analysis pipelines. It is therefore important that visualisations can be quickly prototyped, as well as developed or embedded into full applications. In order to better judge spatiotemporal relationships, immersive hardware, such as Virtual or Augmented Reality (VR/AR) headsets and associated controllers are becoming invaluable tools. In this work we present scenery, a modular and extensible visualisation framework for the Java VM that can handle mesh and large volumetric data, containing multiple views, timepoints, and color channels. scenery is free and open-source software, works on all major platforms, and uses the Vulkan or OpenGL rendering APIs. We introduce scenery's main features, and discuss its use with VR/AR hardware and in distributed rendering. In addition to the visualisation framework, we present a series of case studies, where scenery can provide tangible benefit in developmental and systems biology: With Bionic Tracking, we demonstrate a new technique for tracking cells in 4D volumetric datasets via tracking eye gaze in a virtual reality headset, with the potential to speed up manual tracking tasks by an order of magnitude. We further introduce ideas to move towards virtual reality-based laser ablation and perform a user study in order to gain insight into performance, acceptance and issues when performing ablation tasks with virtual reality hardware in fast developing specimen. To tame the amount of data originating from state-of-the-art volumetric microscopes, we present ideas how to render the highly-efficient Adaptive Particle Representation, and finally, we present sciview, an ImageJ2/Fiji plugin making the features of scenery available to a wider audience.:Abstract Foreword and Acknowledgements Overview and Contributions Part 1 - Introduction 1 Fluorescence Microscopy 2 Introduction to Visual Processing 3 A Short Introduction to Cross Reality 4 Eye Tracking and Gaze-based Interaction Part 2 - VR and AR for System Biology 5 scenery — VR/AR for Systems Biology 6 Rendering 7 Input Handling and Integration of External Hardware 8 Distributed Rendering 9 Miscellaneous Subsystems 10 Future Development Directions Part III - Case Studies C A S E S T U D I E S 11 Bionic Tracking: Using Eye Tracking for Cell Tracking 12 Towards Interactive Virtual Reality Laser Ablation 13 Rendering the Adaptive Particle Representation 14 sciview — Integrating scenery into ImageJ2 & Fiji Part IV - Conclusion 15 Conclusions and Outlook Backmatter & Appendices A Questionnaire for VR Ablation User Study B Full Correlations in VR Ablation Questionnaire C Questionnaire for Bionic Tracking User Study List of Tables List of Figures Bibliography Selbstständigkeitserklärun

Motion Parallax in Stereo 3D: Model and Applications

Binocular disparity is the main depth cue that makes stereoscopic images appear 3D. However, in many scenarios, the range of depth that can be reproduced by this cue is greatly limited and typically fixed due to constraints imposed by displays. For example, due to the low angular resolution of current automultiscopic screens, they can only reproduce a shallow depth range. In this work, we study the motion parallax cue, which is a relatively strong depth cue, and can be freely reproduced even on a 2D screen without any limits. We exploit the fact that in many practical scenarios, motion parallax provides sufficiently strong depth information that the presence of binocular depth cues can be reduced through aggressive disparity compression. To assess the strength of the effect we conduct psycho-visual experiments that measure the influence of motion parallax on depth perception and relate it to the depth resulting from binocular disparity. Based on the measurements, we propose a joint disparity-parallax computational model that predicts apparent depth resulting from both cues. We demonstrate how this model can be applied in the context of stereo and multiscopic image processing, and propose new disparity manipulation techniques, which first quantify depth obtained from motion parallax, and then adjust binocular disparity information accordingly. This allows us to manipulate the disparity signal according to the strength of motion parallax to improve the overall depth reproduction. This technique is validated in additional experiments

Quality of Experience in Immersive Video Technologies

Over the last decades, several technological revolutions have impacted the television industry, such as the shifts from black & white to color and from standard to high-definition. Nevertheless, further considerable improvements can still be achieved to provide a better multimedia experience, for example with ultra-high-definition, high dynamic range & wide color gamut, or 3D. These so-called immersive technologies aim at providing better, more realistic, and emotionally stronger experiences. To measure quality of experience (QoE), subjective evaluation is the ultimate means since it relies on a pool of human subjects. However, reliable and meaningful results can only be obtained if experiments are properly designed and conducted following a strict methodology. In this thesis, we build a rigorous framework for subjective evaluation of new types of image and video content. We propose different procedures and analysis tools for measuring QoE in immersive technologies. As immersive technologies capture more information than conventional technologies, they have the ability to provide more details, enhanced depth perception, as well as better color, contrast, and brightness. To measure the impact of immersive technologies on the viewersâ QoE, we apply the proposed framework for designing experiments and analyzing collected subjectsâ ratings. We also analyze eye movements to study human visual attention during immersive content playback. Since immersive content carries more information than conventional content, efficient compression algorithms are needed for storage and transmission using existing infrastructures. To determine the required bandwidth for high-quality transmission of immersive content, we use the proposed framework to conduct meticulous evaluations of recent image and video codecs in the context of immersive technologies. Subjective evaluation is time consuming, expensive, and is not always feasible. Consequently, researchers have developed objective metrics to automatically predict quality. To measure the performance of objective metrics in assessing immersive content quality, we perform several in-depth benchmarks of state-of-the-art and commonly used objective metrics. For this aim, we use ground truth quality scores, which are collected under our subjective evaluation framework. To improve QoE, we propose different systems for stereoscopic and autostereoscopic 3D displays in particular. The proposed systems can help reducing the artifacts generated at the visualization stage, which impact picture quality, depth quality, and visual comfort. To demonstrate the effectiveness of these systems, we use the proposed framework to measure viewersâ preference between these systems and standard 2D & 3D modes. In summary, this thesis tackles the problems of measuring, predicting, and improving QoE in immersive technologies. To address these problems, we build a rigorous framework and we apply it through several in-depth investigations. We put essential concepts of multimedia QoE under this framework. These concepts not only are of fundamental nature, but also have shown their impact in very practical applications. In particular, the JPEG, MPEG, and VCEG standardization bodies have adopted these concepts to select technologies that were proposed for standardization and to validate the resulting standards in terms of compression efficiency

Perceptual modelling for 2D and 3D

Livrable D1.1 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D1.1 du projet

Efficient image-based rendering

Recent advancements in real-time ray tracing and deep learning have significantly enhanced the realism of computer-generated images. However, conventional 3D computer graphics (CG) can still be time-consuming and resource-intensive, particularly when creating photo-realistic simulations of complex or animated scenes. Image-based rendering (IBR) has emerged as an alternative approach that utilizes pre-captured images from the real world to generate realistic images in real-time, eliminating the need for extensive modeling. Although IBR has its advantages, it faces challenges in providing the same level of control over scene attributes as traditional CG pipelines and accurately reproducing complex scenes and objects with different materials, such as transparent objects. This thesis endeavors to address these issues by harnessing the power of deep learning and incorporating the fundamental principles of graphics and physical-based rendering. It offers an efficient solution that enables interactive manipulation of real-world dynamic scenes captured from sparse views, lighting positions, and times, as well as a physically-based approach that facilitates accurate reproduction of the view dependency effect resulting from the interaction between transparent objects and their surrounding environment. Additionally, this thesis develops a visibility metric that can identify artifacts in the reconstructed IBR images without observing the reference image, thereby contributing to the design of an effective IBR acquisition pipeline. Lastly, a perception-driven rendering technique is developed to provide high-fidelity visual content in virtual reality displays while retaining computational efficiency.Jüngste Fortschritte im Bereich Echtzeit-Raytracing und Deep Learning haben den Realismus computergenerierter Bilder erheblich verbessert. Konventionelle 3DComputergrafik (CG) kann jedoch nach wie vor zeit- und ressourcenintensiv sein, insbesondere bei der Erstellung fotorealistischer Simulationen von komplexen oder animierten Szenen. Das bildbasierte Rendering (IBR) hat sich als alternativer Ansatz herauskristallisiert, bei dem vorab aufgenommene Bilder aus der realen Welt verwendet werden, um realistische Bilder in Echtzeit zu erzeugen, so dass keine umfangreiche Modellierung erforderlich ist. Obwohl IBR seine Vorteile hat, ist es eine Herausforderung, das gleiche Maß an Kontrolle über Szenenattribute zu bieten wie traditionelle CG-Pipelines und komplexe Szenen und Objekte mit unterschiedlichen Materialien, wie z.B. transparente Objekte, akkurat wiederzugeben. In dieser Arbeit wird versucht, diese Probleme zu lösen, indem die Möglichkeiten des Deep Learning genutzt und die grundlegenden Prinzipien der Grafik und des physikalisch basierten Renderings einbezogen werden. Sie bietet eine effiziente Lösung, die eine interaktive Manipulation von dynamischen Szenen aus der realen Welt ermöglicht, die aus spärlichen Ansichten, Beleuchtungspositionen und Zeiten erfasst wurden, sowie einen physikalisch basierten Ansatz, der eine genaue Reproduktion des Effekts der Sichtabhängigkeit ermöglicht, der sich aus der Interaktion zwischen transparenten Objekten und ihrer Umgebung ergibt. Darüber hinaus wird in dieser Arbeit eine Sichtbarkeitsmetrik entwickelt, mit der Artefakte in den rekonstruierten IBR-Bildern identifiziert werden können, ohne das Referenzbild zu betrachten, und die somit zur Entwicklung einer effektiven IBR-Erfassungspipeline beiträgt. Schließlich wird ein wahrnehmungsgesteuertes Rendering-Verfahren entwickelt, um visuelle Inhalte in Virtual-Reality-Displays mit hoherWiedergabetreue zu liefern und gleichzeitig die Rechenleistung zu erhalten