Novel Motion Anchoring Strategies for Wavelet-based Highly Scalable Video Compression

This thesis investigates new motion anchoring strategies that are targeted at wavelet-based highly scalable video compression (WSVC). We depart from two practices that are deeply ingrained in existing video compression systems. Instead of the commonly used block motion, which has poor scalability attributes, we employ piecewise-smooth motion together with a highly scalable motion boundary description. The combination of this more “physical” motion description together with motion discontinuity information allows us to change the conventional strategy of anchoring motion at target frames to anchoring motion at reference frames, which improves motion inference across time. In the proposed reference-based motion anchoring strategies, motion fields are mapped from reference to target frames, where they serve as prediction references; during this mapping process, disoccluded regions are readily discovered. Observing that motion discontinuities displace with foreground objects, we propose motion-discontinuity driven motion mapping operations that handle traditionally challenging regions around moving objects. The reference-based motion anchoring exposes an intricate connection between temporal frame interpolation (TFI) and video compression. When employed in a compression system, all anchoring strategies explored in this thesis perform TFI once all residual information is quantized to zero at a given temporal level. The interpolation performance is evaluated on both natural and synthetic sequences, where we show favourable comparisons with state-of-the-art TFI schemes. We explore three reference-based motion anchoring strategies. In the first one, the motion anchoring is “flipped” with respect to a hierarchical B-frame structure. We develop an analytical model to determine the weights of the different spatio-temporal subbands, and assess the suitability and benefits of this reference-based WSVC for (highly scalable) video compression. Reduced motion coding cost and improved frame prediction, especially around moving objects, result in improved rate-distortion performance compared to a target-based WSVC. As the thesis evolves, the motion anchoring is progressively simplified to one where all motion is anchored at one base frame; this central motion organization facilitates the incorporation of higher-order motion models, which improve the prediction performance in regions following motion with non-constant velocity

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

Enforcing Realism and Temporal Consistency for Large-Scale Video Inpainting

Today, people are consuming more videos than ever before. At the same time, video manipulation has rapidly been gaining traction due to the influence of viral videos, as well as the convenience of editing software. Although video manipulation has legitimate entertainment purposes, it can also be incredibly destructive. In order to understand the positive and negative consequences of media manipulation---as well as to maintain the integrity of mass media---it is important to investigate the capabilities of video manipulation techniques. In this dissertation, we focus on the manipulation task of video inpainting, where the goal is to automatically fill in missing parts of a masked video with semantically relevant content. Inpainting results should possess high visual quality with respect to reconstruction performance, realism, and temporal consistency, i.e., they should faithfully recreate missing contents in a way that resembles the real world and exhibits minimal flickering artifacts. Two major challenges have impeded progress toward improving visual quality: semantic ambiguity and diagnostic evaluation. Semantic ambiguity exists for any masked video due to several plausible explanations of the events in the observed scene; however, prior methods have struggled with ambiguity due to their limited temporal contexts. As for diagnostic evaluation, prior work has overemphasized aggregate analysis on large datasets and underemphasized fine-grained analysis on modern inpainting failure modes; as a result, the expected behaviors of models under specific scenarios have remained poorly understood. Our work improves on both models and evaluation techniques for video inpainting, thereby providing deeper insight into how an inpainting model's design impacts the visual quality of its outputs. To advance state-of-the-art in video inpainting, we propose two novel solutions that improve visual quality by expanding the available temporal context. Our first approach, bi-TAI, intelligently integrates information from multiple frames before and after the desired sequence. It produces more realistic results than prior work, which could only consume limited contextual information. Our second approach, HyperCon, suppresses flickering artifacts from frame-wise processing by identifying and propagating consistencies found in high frame-rate space; we successfully apply it to tasks as disparate as video inpainting and style transfer. Aside from methodological improvements, we also propose two novel evaluation tools to diagnose failure modes of modern video inpainting methods. Our first such contribution is the Moving Symbols dataset, which we use to characterize the sensitivity of a state-of-the-art video prediction model to controllable appearance and motion parameters. Our second contribution is the DEVIL benchmark, which provides a dataset and a comprehensive evaluation scheme to quantify how several semantic properties of the input video and mask affect video inpainting quality. Through models that exploit temporal context---as well as evaluation paradigms that reveal fine-grained failure modes of modern inpainting methods at scale---our contributions enforce better visual quality for video inpainting on a larger scale than prior work. We enable the production of more convincing manipulated videos for data processing and social media needs; we also establish replicable fine-grained analysis techniques to cultivate future progress in the field.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169785/1/szetor_1.pd

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

Understanding the Dynamic Visual World: From Motion to Semantics

We live in a dynamic world, which is continuously in motion. Perceiving and interpreting the dynamic surroundings is an essential capability for an intelligent agent. Human beings have the remarkable capability to learn from limited data, with partial or little annotation, in sharp contrast to computational perception models that rely on large-scale, manually labeled data. Reliance on strongly supervised models with manually labeled data inherently prohibits us from modeling the dynamic visual world, as manual annotations are tedious, expensive, and not scalable, especially if we would like to solve multiple scene understanding tasks at the same time. Even worse, in some cases, manual annotations are completely infeasible, such as the motion vector of each pixel (i.e., optical flow) since humans cannot reliably produce these types of labeling. In fact, living in a dynamic world, when we move around, motion information, as a result of moving camera, independently moving objects, and scene geometry, consists of abundant information, revealing the structure and complexity of our dynamic visual world. As the famous psychologist James J. Gibson suggested, “we must perceive in order to move, but we also must move in order to perceive”. In this thesis, we investigate how to use the motion information contained in unlabeled or partially labeled videos to better understand and synthesize the dynamic visual world. This thesis consists of three parts. In the first part, we focus on the “move to perceive” aspect. When moving through the world, it is natural for an intelligent agent to associate image patterns with the magnitude of their displacement over time: as the agent moves, far away mountains don’t move much; nearby trees move a lot. This natural relationship between the appearance of objects and their apparent motion is a rich source of information about the relationship between the distance of objects and their appearance in images. We present a pretext task of estimating the relative depth of elements of a scene (i.e., ordering the pixels in an image according to distance from the viewer) recovered from motion field of unlabeled videos. The goal of this pretext task was to induce useful feature representations in deep Convolutional Neural Networks (CNNs). These induced representations, using 1.1 million video frames crawled from YouTube within one hour without any manual labeling, provide valuable starting features for the training of neural networks for downstream tasks. It is promising to match or even surpass what ImageNet pre-training gives us today, which needs a huge amount of manual labeling, on tasks such as semantic image segmentation as all of our training data comes almost for free. In the second part, we study the “perceive to move” aspect. As we humans look around, we do not solve a single vision task at a time. Instead, we perceive our surroundings in a holistic manner, doing visual understanding using all visual cues jointly. By simultaneously solving multiple tasks together, one task can influence another. In specific, we propose a neural network architecture, called SENSE, which shares common feature representations among four closely-related tasks: optical flow estimation, disparity estimation from stereo, occlusion detection, and semantic segmentation. The key insight is that sharing features makes the network more compact and induces better feature representations. For real-world data, however, not all an- notations of the four tasks mentioned above are always available at the same time. To this end, loss functions are designed to exploit interactions of different tasks and do not need manual annotations, to better handle partially labeled data in a semi- supervised manner, leading to superior understanding performance of the dynamic visual world. Understanding the motion contained in a video enables us to perceive the dynamic visual world in a novel manner. In the third part, we present an approach, called SuperSloMo, which synthesizes slow-motion videos from a standard frame-rate video. Converting a plain video into a slow-motion version enables us to see memorable moments in our life that are hard to see clearly otherwise with naked eyes: a difficult skateboard trick, a dog catching a ball, etc. Such a technique also has wide applications such as generating smooth view transition on a head-mounted virtual reality (VR) devices, compressing videos, synthesizing videos with motion blur, etc

Perceptually-motivated, interactive rendering and editing of global illumination

This thesis proposes several new perceptually-motivated techniques to synthesize, edit and enhance depiction of three-dimensional virtual scenes. Finding algorithms that fit the perceptually economic middle ground between artistic depiction and full physical simulation is the challenge taken in this work. First, we will present three interactive global illumination rendering approaches that are inspired by perception to efficiently depict important light transport. Those methods have in common to compute global illumination in large and fully dynamic scenes allowing for light, geometry, and material changes at interactive or real-time rates. Further, this thesis proposes a tool to edit reflections, that allows to bend physical laws to match artistic goals by exploiting perception. Finally, this work contributes a post-processing operator that depicts high contrast scenes in the same way as artists do, by simulating it "seen'; through a dynamic virtual human eye in real-time.Diese Arbeit stellt eine Anzahl von Algorithmen zur Synthese, Bearbeitung und verbesserten Darstellung von virtuellen drei-dimensionalen Szenen vor. Die Herausforderung liegt dabei in der Suche nach Ausgewogenheit zwischen korrekter physikalischer Berechnung und der künstlerischen, durch die Gesetze der menschlichen Wahrnehmung motivierten Praxis. Zunächst werden drei Verfahren zur Bild-Synthese mit globaler Beleuchtung vorgestellt, deren Gemeinsamkeit in der effizienten Handhabung großer und dynamischer virtueller Szenen liegt, in denen sich Geometrie, Materialen und Licht frei verändern lassen. Darauffolgend wird ein Werkzeug zum Editieren von Reflektionen in virtuellen Szenen das die menschliche Wahrnehmung ausnutzt um künstlerische Vorgaben umzusetzen, vorgestellt. Die Arbeit schließt mit einem Filter am Ende der Verarbeitungskette, der den wahrgenommen Kontrast in einem Bild erhöht, indem er die Entstehung von Glanzeffekten im menschlichen Auge nachbildet