Fully Scalable Video Coding Using Redundant-Wavelet Multihypothesis and Motion-Compensated Temporal Filtering

In this dissertation, a fully scalable video coding system is proposed. This system achieves full temporal, resolution, and fidelity scalability by combining mesh-based motion-compensated temporal filtering, multihypothesis motion compensation, and an embedded 3D wavelet-coefficient coder. The first major contribution of this work is the introduction of the redundant-wavelet multihypothesis paradigm into motion-compensated temporal filtering, which is achieved by deploying temporal filtering in the domain of a spatially redundant wavelet transform. A regular triangle mesh is used to track motion between frames, and an affine transform between mesh triangles implements motion compensation within a lifting-based temporal transform. Experimental results reveal that the incorporation of redundant-wavelet multihypothesis into mesh-based motion-compensated temporal filtering significantly improves the rate-distortion performance of the scalable coder. The second major contribution is the introduction of a sliding-window implementation of motion-compensated temporal filtering such that video sequences of arbitrarily length may be temporally filtered using a finite-length frame buffer without suffering from severe degradation at buffer boundaries. Finally, as a third major contribution, a novel 3D coder is designed for the coding of the 3D volume of coefficients resulting from the redundant-wavelet based temporal filtering. This coder employs an explicit estimate of the probability of coefficient significance to drive a nonadaptive arithmetic coder, resulting in a simple software implementation. Additionally, the coder offers the possibility of a high degree of vectorization particularly well suited to the data-parallel capabilities of modern general-purpose processors or customized hardware. Results show that the proposed coder yields nearly the same rate-distortion performance as a more complicated coefficient coder considered to be state of the art

Motion Estimation and Compensation in the Redundant Wavelet Domain

Despite being the prefered approach for still-image compression for nearly a decade, wavelet-based coding for video has been slow to emerge, due primarily to the fact that the shift variance of the discrete wavelet transform hinders motion estimation and compensation crucial to modern video coders. Recently it has been recognized that a redundant, or overcomplete, wavelet transform is shift invariant and thus permits motion prediction in the wavelet domain. In this dissertation, other uses for the redundancy of overcomplete wavelet transforms in video coding are explored. First, it is demonstrated that the redundant-wavelet domain facilitates the placement of an irregular triangular mesh to video images, thereby exploiting transform redundancy to implement geometries for motion estimation and compensation more general than the traditional block structure widely employed. As the second contribution of this dissertation, a new form of multihypothesis prediction, redundant wavelet multihypothesis, is presented. This new approach to motion estimation and compensation produces motion predictions that are diverse in transform phase to increase prediction accuracy. Finally, it is demonstrated that the proposed redundant-wavelet strategies complement existing advanced video-coding techniques and produce significant performance improvements in a battery of experimental results

Mesh-based video coding for low bit-rate communications

In this paper, a new method for low bit-rate content-adaptive mesh-based video coding is proposed. Intra-frame coding of this method employs feature map extraction for node distribution at specific threshold levels to achieve higher density placement of initial nodes for regions that contain high frequency features and conversely sparse placement of initial nodes for smooth regions. Insignificant nodes are largely removed using a subsequent node elimination scheme. The Hilbert scan is then applied before quantization and entropy coding to reduce amount of transmitted information. For moving images, both node position and color parameters of only a subset of nodes may change from frame to frame. It is sufficient to transmit only these changed parameters. The proposed method is well-suited for video coding at very low bit rates, as processing results demonstrate that it provides good subjective and objective image quality at a lower number of required bits

Representation and coding of 3D video data

Livrable D4.1 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 D4.1 du projet

GPU data structures for graphics and vision

Graphics hardware has in recent years become increasingly programmable, and its programming APIs use the stream processor model to expose massive parallelization to the programmer. Unfortunately, the inherent restrictions of the stream processor model, used by the GPU in order to maintain high performance, often pose a problem in porting CPU algorithms for both video and volume processing to graphics hardware. Serial data dependencies which accelerate CPU processing are counterproductive for the data-parallel GPU. This thesis demonstrates new ways for tackling well-known problems of large scale video/volume analysis. In some instances, we enable processing on the restricted hardware model by re-introducing algorithms from early computer graphics research. On other occasions, we use newly discovered, hierarchical data structures to circumvent the random-access read/fixed write restriction that had previously kept sophisticated analysis algorithms from running solely on graphics hardware. For 3D processing, we apply known game graphics concepts such as mip-maps, projective texturing, and dependent texture lookups to show how video/volume processing can benefit algorithmically from being implemented in a graphics API. The novel GPU data structures provide drastically increased processing speed, and lift processing heavy operations to real-time performance levels, paving the way for new and interactive vision/graphics applications.Graphikhardware wurde in den letzen Jahren immer weiter programmierbar. Ihre APIs verwenden das Streamprozessor-Modell, um die massive Parallelisierung auch für den Programmierer verfügbar zu machen. Leider folgen aus dem strikten Streamprozessor-Modell, welches die GPU für ihre hohe Rechenleistung benötigt, auch Hindernisse in der Portierung von CPU-Algorithmen zur Video- und Volumenverarbeitung auf die GPU. Serielle Datenabhängigkeiten beschleunigen zwar CPU-Verarbeitung, sind aber für die daten-parallele GPU kontraproduktiv . Diese Arbeit präsentiert neue Herangehensweisen für bekannte Probleme der Video- und Volumensverarbeitung. Teilweise wird die Verarbeitung mit Hilfe von modifizierten Algorithmen aus der frühen Computergraphik-Forschung an das beschränkte Hardwaremodell angepasst. Anderswo helfen neu entdeckte, hierarchische Datenstrukturen beim Umgang mit den Schreibzugriff-Restriktionen die lange die Portierung von komplexeren Bildanalyseverfahren verhindert hatten. In der 3D-Verarbeitung nutzen wir bekannte Konzepte aus der Computerspielegraphik wie Mipmaps, projektive Texturierung, oder verkettete Texturzugriffe, und zeigen auf welche Vorteile die Video- und Volumenverarbeitung aus hardwarebeschleunigter Graphik-API-Implementation ziehen kann. Die präsentierten GPU-Datenstrukturen bieten drastisch schnellere Verarbeitung und heben rechenintensive Operationen auf Echtzeit-Niveau. Damit werden neue, interaktive Bildverarbeitungs- und Graphik-Anwendungen möglich

Toward sparse and geometry adapted video approximations

Video signals are sequences of natural images, where images are often modeled as piecewise-smooth signals. Hence, video can be seen as a 3D piecewise-smooth signal made of piecewise-smooth regions that move through time. Based on the piecewise-smooth model and on related theoretical work on rate-distortion performance of wavelet and oracle based coding schemes, one can better analyze the appropriate coding strategies that adaptive video codecs need to implement in order to be efficient. Efficient video representations for coding purposes require the use of adaptive signal decompositions able to capture appropriately the structure and redundancy appearing in video signals. Adaptivity needs to be such that it allows for proper modeling of signals in order to represent these with the lowest possible coding cost. Video is a very structured signal with high geometric content. This includes temporal geometry (normally represented by motion information) as well as spatial geometry. Clearly, most of past and present strategies used to represent video signals do not exploit properly its spatial geometry. Similarly to the case of images, a very interesting approach seems to be the decomposition of video using large over-complete libraries of basis functions able to represent salient geometric features of the signal. In the framework of video, these features should model 2D geometric video components as well as their temporal evolution, forming spatio-temporal 3D geometric primitives. Through this PhD dissertation, different aspects on the use of adaptivity in video representation are studied looking toward exploiting both aspects of video: its piecewise nature and the geometry. The first part of this work studies the use of localized temporal adaptivity in subband video coding. This is done considering two transformation schemes used for video coding: 3D wavelet representations and motion compensated temporal filtering. A theoretical R-D analysis as well as empirical results demonstrate how temporal adaptivity improves coding performance of moving edges in 3D transform (without motion compensation) based video coding. Adaptivity allows, at the same time, to equally exploit redundancy in non-moving video areas. The analogy between motion compensated video and 1D piecewise-smooth signals is studied as well. This motivates the introduction of local length adaptivity within frame-adaptive motion compensated lifted wavelet decompositions. This allows an optimal rate-distortion performance when video motion trajectories are shorter than the transformation "Group Of Pictures", or when efficient motion compensation can not be ensured. After studying temporal adaptivity, the second part of this thesis is dedicated to understand the fundamentals of how can temporal and spatial geometry be jointly exploited. This work builds on some previous results that considered the representation of spatial geometry in video (but not temporal, i.e, without motion). In order to obtain flexible and efficient (sparse) signal representations, using redundant dictionaries, the use of highly non-linear decomposition algorithms, like Matching Pursuit, is required. General signal representation using these techniques is still quite unexplored. For this reason, previous to the study of video representation, some aspects of non-linear decomposition algorithms and the efficient decomposition of images using Matching Pursuits and a geometric dictionary are investigated. A part of this investigation concerns the study on the influence of using a priori models within approximation non-linear algorithms. Dictionaries with a high internal coherence have some problems to obtain optimally sparse signal representations when used with Matching Pursuits. It is proved, theoretically and empirically, that inserting in this algorithm a priori models allows to improve the capacity to obtain sparse signal approximations, mainly when coherent dictionaries are used. Another point discussed in this preliminary study, on the use of Matching Pursuits, concerns the approach used in this work for the decompositions of video frames and images. The technique proposed in this thesis improves a previous work, where authors had to recur to sub-optimal Matching Pursuit strategies (using Genetic Algorithms), given the size of the functions library. In this work the use of full search strategies is made possible, at the same time that approximation efficiency is significantly improved and computational complexity is reduced. Finally, a priori based Matching Pursuit geometric decompositions are investigated for geometric video representations. Regularity constraints are taken into account to recover the temporal evolution of spatial geometric signal components. The results obtained for coding and multi-modal (audio-visual) signal analysis, clarify many unknowns and show to be promising, encouraging to prosecute research on the subject

Compression of dynamic polygonal meshes with constant and variable connectivity

This work was supported by the projects 20-02154S and 17-07690S of the Czech Science Foundation and SGS-2019-016 of the Czech Ministry of Education.Polygonal mesh sequences with variable connectivity are incredibly versatile dynamic surface representations as they allow a surface to change topology or details to suddenly appear or disappear. This, however, comes at the cost of large storage size. Current compression methods inefficiently exploit the temporal coherence of general data because the correspondences between two subsequent frames might not be bijective. We study the current state of the art including the special class of mesh sequences for which connectivity is static. We also focus on the state of the art of a related field of dynamic point cloud sequences. Further, we point out parts of the compression pipeline with the possibility of improvement. We present the progress we have already made in designing a temporal model capturing the temporal coherence of the sequence, and point out to directions for a future research

On unifying sparsity and geometry for image-based 3D scene representation

Demand has emerged for next generation visual technologies that go beyond conventional 2D imaging. Such technologies should capture and communicate all perceptually relevant three-dimensional information about an environment to a distant observer, providing a satisfying, immersive experience. Camera networks offer a low cost solution to the acquisition of 3D visual information, by capturing multi-view images from different viewpoints. However, the camera's representation of the data is not ideal for common tasks such as data compression or 3D scene analysis, as it does not make the 3D scene geometry explicit. Image-based scene representations fundamentally require a multi-view image model that facilitates extraction of underlying geometrical relationships between the cameras and scene components. Developing new, efficient multi-view image models is thus one of the major challenges in image-based 3D scene representation methods. This dissertation focuses on defining and exploiting a new method for multi-view image representation, from which the 3D geometry information is easily extractable, and which is additionally highly compressible. The method is based on sparse image representation using an overcomplete dictionary of geometric features, where a single image is represented as a linear combination of few fundamental image structure features (edges for example). We construct the dictionary by applying a unitary operator to an analytic function, which introduces a composition of geometric transforms (translations, rotation and anisotropic scaling) to that function. The advantage of this approach is that the features across multiple views can be related with a single composition of transforms. We then establish a connection between image components and scene geometry by defining the transforms that satisfy the multi-view geometry constraint, and obtain a new geometric multi-view correlation model. We first address the construction of dictionaries for images acquired by omnidirectional cameras, which are particularly convenient for scene representation due to their wide field of view. Since most omnidirectional images can be uniquely mapped to spherical images, we form a dictionary by applying motions on the sphere, rotations, and anisotropic scaling to a function that lives on the sphere. We have used this dictionary and a sparse approximation algorithm, Matching Pursuit, for compression of omnidirectional images, and additionally for coding 3D objects represented as spherical signals. Both methods offer better rate-distortion performance than state of the art schemes at low bit rates. The novel multi-view representation method and the dictionary on the sphere are then exploited for the design of a distributed coding method for multi-view omnidirectional images. In a distributed scenario, cameras compress acquired images without communicating with each other. Using a reliable model of correlation between views, distributed coding can achieve higher compression ratios than independent compression of each image. However, the lack of a proper model has been an obstacle for distributed coding in camera networks for many years. We propose to use our geometric correlation model for distributed multi-view image coding with side information. The encoder employs a coset coding strategy, developed by dictionary partitioning based on atom shape similarity and multi-view geometry constraints. Our method results in significant rate savings compared to independent coding. An additional contribution of the proposed correlation model is that it gives information about the scene geometry, leading to a new camera pose estimation method using an extremely small amount of data from each camera. Finally, we develop a method for learning stereo visual dictionaries based on the new multi-view image model. Although dictionary learning for still images has received a lot of attention recently, dictionary learning for stereo images has been investigated only sparingly. Our method maximizes the likelihood that a set of natural stereo images is efficiently represented with selected stereo dictionaries, where the multi-view geometry constraint is included in the probabilistic modeling. Experimental results demonstrate that including the geometric constraints in learning leads to stereo dictionaries that give both better distributed stereo matching and approximation properties than randomly selected dictionaries. We show that learning dictionaries for optimal scene representation based on the novel correlation model improves the camera pose estimation and that it can be beneficial for distributed coding

Deformable shape matching

Deformable shape matching has become an important building block in academia as well as in industry. Given two three dimensional shapes A and B the deformation function f aligning A with B has to be found. The function is discretized by a set of corresponding point pairs. Unfortunately, the computation cost of a brute-force search of correspondences is exponential. Additionally, to be of any practical use the algorithm has to be able to deal with data coming directly from 3D scanner devices which suffers from acquisition problems like noise, holes as well as missing any information about topology. This dissertation presents novel solutions for solving shape matching: First, an algorithm estimating correspondences using a randomized search strategy is shown. Additionally, a planning step dramatically reducing the matching costs is incorporated. Using ideas of these both contributions, a method for matching multiple shapes at once is shown. The method facilitates the reconstruction of shape and motion from noisy data acquired with dynamic 3D scanners. Considering shape matching from another perspective a solution is shown using Markov Random Fields (MRF). Formulated as MRF, partial as well as full matches of a shape can be found. Here, belief propagation is utilized for inference computation in the MRF. Finally, an approach significantly reducing the space-time complexity of belief propagation for a wide spectrum of computer vision tasks is presented.Anpassung deformierbarer Formen ist zu einem wichtigen Baustein in der akademischen Welt sowie in der Industrie geworden. Gegeben zwei dreidimensionale Formen A und B, suchen wir nach einer Verformungsfunktion f, die die Deformation von A auf B abbildet. Die Funktion f wird durch eine Menge von korrespondierenden Punktepaaren diskretisiert. Leider sind die Berechnungskosten für eine Brute-Force-Suche dieser Korrespondenzen exponentiell. Um zusätzlich von einem praktischen Nutzen zu sein, muss der Suchalgorithmus in der Lage sein, mit Daten, die direkt aus 3D-Scanner kommen, umzugehen. Bedauerlicherweise leiden diese Daten unter Akquisitionsproblemen wie Rauschen, Löcher sowie fehlender Topologieinformation. In dieser Dissertation werden neue Lösungen für das Problem der Formanpassung präsentiert. Als erstes wird ein Algorithmus gezeigt, der die Korrespondenzen mittels einer randomisierten Suchstrategie schätzt. Zusätzlich wird anhand eines automatisch berechneten Schätzplanes die Geschwindigkeit der Suchstrategie verbessert. Danach wird ein Verfahren gezeigt, dass die Anpassung mehrerer Formen gleichzeitig bewerkstelligen kann. Diese Methode ermöglicht es, die Bewegung, sowie die eigentliche Struktur des Objektes aus verrauschten Daten, die mittels dynamischer 3D-Scanner aufgenommen wurden, zu rekonstruieren. Darauffolgend wird das Problem der Formanpassung aus einer anderen Perspektive betrachtet und als Markov-Netzwerk (MRF) reformuliert. Dieses ermöglicht es, die Formen auch stückweise aufeinander abzubilden. Die eigentliche Lösung wird mittels Belief Propagation berechnet. Schließlich wird ein Ansatz gezeigt, der die Speicher-Zeit-Komplexität von Belief Propagation für ein breites Spektrum von Computer-Vision Problemen erheblich reduziert