Transformées basées graphes pour la compression de nouvelles modalités d’image

Due to the large availability of new camera types capturing extra geometrical information, as well as the emergence of new image modalities such as light fields and omni-directional images, a huge amount of high dimensional data has to be stored and delivered. The ever growing streaming and storage requirements of these new image modalities require novel image coding tools that exploit the complex structure of those data. This thesis aims at exploring novel graph based approaches for adapting traditional image transform coding techniques to the emerging data types where the sampled information are lying on irregular structures. In a first contribution, novel local graph based transforms are designed for light field compact representations. By leveraging a careful design of local transform supports and a local basis functions optimization procedure, significant improvements in terms of energy compaction can be obtained. Nevertheless, the locality of the supports did not permit to exploit long term dependencies of the signal. This led to a second contribution where different sampling strategies are investigated. Coupled with novel prediction methods, they led to very prominent results for quasi-lossless compression of light fields. The third part of the thesis focuses on the definition of rate-distortion optimized sub-graphs for the coding of omni-directional content. If we move further and give more degree of freedom to the graphs we wish to use, we can learn or define a model (set of weights on the edges) that might not be entirely reliable for transform design. The last part of the thesis is dedicated to theoretically analyze the effect of the uncertainty on the efficiency of the graph transforms.En raison de la grande disponibilité de nouveaux types de caméras capturant des informations géométriques supplémentaires, ainsi que de l'émergence de nouvelles modalités d'image telles que les champs de lumière et les images omnidirectionnelles, il est nécessaire de stocker et de diffuser une quantité énorme de hautes dimensions. Les exigences croissantes en matière de streaming et de stockage de ces nouvelles modalités d’image nécessitent de nouveaux outils de codage d’images exploitant la structure complexe de ces données. Cette thèse a pour but d'explorer de nouvelles approches basées sur les graphes pour adapter les techniques de codage de transformées d'image aux types de données émergents où les informations échantillonnées reposent sur des structures irrégulières. Dans une première contribution, de nouvelles transformées basées sur des graphes locaux sont conçues pour des représentations compactes des champs de lumière. En tirant parti d’une conception minutieuse des supports de transformées locaux et d’une procédure d’optimisation locale des fonctions de base , il est possible d’améliorer considérablement le compaction d'énergie. Néanmoins, la localisation des supports ne permettait pas d'exploiter les dépendances à long terme du signal. Cela a conduit à une deuxième contribution où différentes stratégies d'échantillonnage sont étudiées. Couplés à de nouvelles méthodes de prédiction, ils ont conduit à des résultats très importants en ce qui concerne la compression quasi sans perte de champs de lumière statiques. La troisième partie de la thèse porte sur la définition de sous-graphes optimisés en distorsion de débit pour le codage de contenu omnidirectionnel. Si nous allons plus loin et donnons plus de liberté aux graphes que nous souhaitons utiliser, nous pouvons apprendre ou définir un modèle (ensemble de poids sur les arêtes) qui pourrait ne pas être entièrement fiable pour la conception de transformées. La dernière partie de la thèse est consacrée à l'analyse théorique de l'effet de l'incertitude sur l'efficacité des transformées basées graphes

Dense light field coding: a survey

Light Field (LF) imaging is a promising solution for providing more immersive and closer to reality multimedia experiences to end-users with unprecedented creative freedom and flexibility for applications in different areas, such as virtual and augmented reality. Due to the recent technological advances in optics, sensor manufacturing and available transmission bandwidth, as well as the investment of many tech giants in this area, it is expected that soon many LF transmission systems will be available to both consumers and professionals. Recognizing this, novel standardization initiatives have recently emerged in both the Joint Photographic Experts Group (JPEG) and the Moving Picture Experts Group (MPEG), triggering the discussion on the deployment of LF coding solutions to efficiently handle the massive amount of data involved in such systems. Since then, the topic of LF content coding has become a booming research area, attracting the attention of many researchers worldwide. In this context, this paper provides a comprehensive survey of the most relevant LF coding solutions proposed in the literature, focusing on angularly dense LFs. Special attention is placed on a thorough description of the different LF coding methods and on the main concepts related to this relevant area. Moreover, comprehensive insights are presented into open research challenges and future research directions for LF coding.info:eu-repo/semantics/publishedVersio

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

Optimal layered representation for adaptive interactive multiview video streaming

We consider an interactive multiview video streaming (IMVS) system where clients select their preferred viewpoint in a given navigation window. To provide high quality IMVS, many high quality views should be transmitted to the clients. However, this is not always possible due to the limited and heterogeneous capabilities of the clients. In this paper, we propose a novel adaptive IMVS solution based on a layered multiview representation where camera views are organized into layered subsets to match the different clients constraints. We formulate an optimization problem for the joint selection of the views subsets and their encoding rates. Then, we propose an optimal and a reduced computational complexity greedy algorithms, both based on dynamic-programming. Simulation results show the good performance of our novel algorithms compared to a baseline algorithm, proving that an effective IMVS adaptive solution should consider the scene content and the client capabilities and their preferences in navigation

Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

Efficient and Accurate Disparity Estimation from MLA-Based Plenoptic Cameras

This manuscript focuses on the processing images from microlens-array based plenoptic cameras. These cameras enable the capturing of the light field in a single shot, recording a greater amount of information with respect to conventional cameras, allowing to develop a whole new set of applications. However, the enhanced information introduces additional challenges and results in higher computational effort. For one, the image is composed of thousand of micro-lens images, making it an unusual case for standard image processing algorithms. Secondly, the disparity information has to be estimated from those micro-images to create a conventional image and a three-dimensional representation. Therefore, the work in thesis is devoted to analyse and propose methodologies to deal with plenoptic images. A full framework for plenoptic cameras has been built, including the contributions described in this thesis. A blur-aware calibration method to model a plenoptic camera, an optimization method to accurately select the best microlenses combination, an overview of the different types of plenoptic cameras and their representation. Datasets consisting of both real and synthetic images have been used to create a benchmark for different disparity estimation algorithm and to inspect the behaviour of disparity under different compression rates. A robust depth estimation approach has been developed for light field microscopy and image of biological samples