Rate-Distortion Optimized Super-Ray Merging for Light Field Compression

International audienceIn this paper, we focus on the problem of compressing dense light fields which represent very large volumes of highly redundant data. In our scheme, view synthesis based on convolutional neural networks (CNN) is used as a first prediction step to exploit interview correlation. Super-rays are then constructed to capture the interview and spatial redundancy remaining in the prediction residues. To ensure that the super-ray segmentation is highly correlated with the residues to be encoded, the super-rays are computed on synthesized residues (the difference between the four transmitted corner views and their corresponding synthesized views), instead of the synthesized views. Neighboring super-rays are merged into a larger super-ray according to a rate-distortion cost. A 4D shape adaptive discrete cosine transform (SA-DCT) is applied per super-ray on the prediction residues in both the spatial and angular dimensions. A traditional coding scheme consisting of quantization and entropy coding is then used for encoding the transformed coefficients. Experimental results show that the proposed coding scheme outperforms HEVC-based schemes at low bitrate

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

Rate-Distortion Optimized Super-Ray Merging for Light Field Compression

International audienceIn this paper, we focus on the problem of compressing dense light fields which represent very large volumes of highly redundant data. In our scheme, view synthesis based on convolutional neural networks (CNN) is used as a first prediction step to exploit interview correlation. Super-rays are then constructed to capture the interview and spatial redundancy remaining in the prediction residues. To ensure that the super-ray segmentation is highly correlated with the residues to be encoded, the super-rays are computed on synthesized residues (the difference between the four transmitted corner views and their corresponding synthesized views), instead of the synthesized views. Neighboring super-rays are merged into a larger super-ray according to a rate-distortion cost. A 4D shape adaptive discrete cosine transform (SA-DCT) is applied per super-ray on the prediction residues in both the spatial and angular dimensions. A traditional coding scheme consisting of quantization and entropy coding is then used for encoding the transformed coefficients. Experimental results show that the proposed coding scheme outperforms HEVC-based schemes at low bitrate

Compression and visual quality assessment for light field contents

Since its invention in the 19th century, photography has allowed to create durable images of the world around us by capturing the intensity of light that flows through a scene, first analogically by using light-sensitive material, and then, with the advent of electronic image sensors, digitally. However, one main limitation of both analog and digital photography lays in its inability to capture any information about the direction of light rays. Through traditional photography, each three-dimensional scene is projected onto a 2D plane; consequently, no information about the position of the 3D objects in space is retained. Light field photography aims at overcoming these limitations by recording the direction of light along with its intensity. In the past, several acquisition technologies have been presented to properly capture light field information, and portable devices have been commercialized to the general public. However, a considerably larger volume of data is generated when compared to traditional photography. Thus, new solutions must be designed to face the challenges light field photography poses in terms of storage, representation, and visualization of the acquired data. In particular, new and efficient compression algorithms are needed to sensibly reduce the amount of data that needs to be stored and transmitted, while maintaining an adequate level of perceptual quality. In designing new solutions to address the unique challenges posed by light field photography, one cannot forgo the importance of having reliable, reproducible means of evaluating their performance, especially in relation to the scenario in which they will be consumed. To that end, subjective assessment of visual quality is of paramount importance to evaluate the impact of compression, representation, and rendering models on user experience. Yet, the standardized methodologies that are commonly used to evaluate the visual quality of traditional media content, such as images and videos, are not equipped to tackle the challenges posed by light field photography. New subjective methodologies must be tailored for the new possibilities this new type of imaging offers in terms of rendering and visual experience. In this work, we address the aforementioned problems by both designing new methodologies for visual quality evaluation of light field contents, and outlining a new compression solution to efficiently reduce the amount of data that needs to be transmitted and stored. We first analyse how traditional methodologies for subjective evaluation of multimedia contents can be adapted to suit light field data, and, we propose new methodologies to reliably assess the visual quality while maintaining user engagement. Furthermore, we study how user behavior is affected by the visual quality of the data. We employ subjective quality assessment to compare several state-of-the-art solutions in light field coding, in order to find the most promising approaches to minimize the volume of data without compromising on the perceptual quality. To that means, we define and inspect several coding approaches for light field compression, and we investigate the impact of color subsampling on the final rendered content. Lastly, we propose a new coding approach to perform light field compression, showing significant improvement with respect to the state of the art