Ces dernières années, les contenus vidéos ont évolué très rapidement. En effet, les télévisions (TV) ont rapidement évolué vers l’Ultra Haute résolution (UHD), la Haute Fréquence d’images (HFR) ou la stéréoscopie (3D). La tendance actuelle est à l’imagerie à Haute Dynamique de luminance (HDR). Ces technologies permettent de reproduire des images beaucoup plus lumineuses que celles des écrans actuels. Chacune de ces améliorations représente une augmentation du coût de stockage et nécessite la création de nouveaux standards de compression vidéo, toujours plus performant. La majorité des consommateurs est actuellement équipé de TV ayant une Dynamique Standard (SDR) qui ne supportent pas les contenus HDR et ils vont lentement renouveler leurs écrans pour un HDR. Il est donc important de délivrer un signal HDR qui puisse être décodé par ces deux types d’écrans. Cette rétro compatibilité est rendue possible par un outil appelé TMO (Tone Mapping Operator) qui transforme un contenu HDR en une version SDR. Au travers de cette thèse, nous explorons de nouvelles méthodes pour améliorer la compression HDR rétro compatible. Premièrement, nous concevons un TMO qui optimise les performances d’un schéma de compression scalable où une couche de base et d’amélioration sont envoyées pour reconstruire les contenus HDR et SDR. Il est démontré que le TMO optimal dépend seulement de la couche SDR de base et que le problème de minimisation peut être séparé en deux étapes consécutives. Pour ces raisons, nous proposons ensuite un autre TMO conçu pour optimiser les performances d’un schéma de compression utilisant uniquement une couche de base mais avec un modèle amélioré et plus précis. Ces deux travaux optimisent des TMO pour images fixes. Par la suite, la thèse se concentre sur l’optimisation de TMO spécifiques à la vidéo. Cependant, on y démontre que l’utilisation d’une prédiction pondérée pour la compression SDR est aussi bon voir meilleur que d’utiliser un TMO optimisé temporellement. Pour ces raisons, un nouvel algorithme et de nouveaux modes de prédictions pondérées sont proposés pour gérer plus efficacement la large diversité des changements lumineux dans les séquences vidéos. / In recent years, video content evolved very quickly. Indeed, televisions (TV) quickly evolved to Ultra High Definition (UHD), High Frame Rate (HFR) or stereoscopy (3D). The recent trend is towards High Dynamic range (HDR). These new technologies allow the reproduction of much brighter images than for actual displays. Each of these improvements represents an increase in storage cost and therefore requires the creation of new video compression standards, always more efficient. The majority of consumers are currently equipped with Standard Dynamic Range (SDR) displays, that cannot handle HDR content. Consumers will slowly renew their display to an HDR one and it is therefore of great importance to deliver an HDR signal that can be decoded by both SDR and HDR displays. Such backward compatibility is provided by a tool called Tone Mapping Operator (TMO) which transforms an HDR content into an SDR version. In this thesis, we explore new methods to improve the backward compatible HDR compression. First, we design a Tone Mapping to optimize scalable compression scheme performances where a base and an enhancement layer are sent to reconstruct the SDR and HDR content. It is demonstrated that the optimum TMO only depends on the SDR base layer and that the minimization problem can be separated in two consecutive minimization steps. Based on these observations, we then propose another TMO designed to optimize the performances of compression schemes using only a base layer but with an enhanced and more precise model. Both of these works optimize TMO for still images. Thereafter, this thesis focuses on the optimization of video-specific TMO. However, we demonstrate that using a weighted prediction for the SDR compression is as good or even better than using a temporally optimized TMO. Therefore, we proposed a new weighted prediction algorithm and new weighted prediction modes to handle more efficiently the large diversity of brightness variations in video sequences.

Les technologies d'écran ont connu récemment une évolution rapide. De la télévision 3D à l'Ultra Haute Définition, la tendance est maintenant aux écrans HDR (pour ''High Dynamic Range'') permettant de reproduire une gamme de luminance bien plus élevée que les écrans classiques. L'émergence de cette technologie implique de nouveaux travaux de standardisation dans le domaine de la compression vidéo. Une question essentielle pour la distribution à grande échelle de contenu HDR est celle de la rétro-compatibilité. Tandis que la future génération d'écrans de télévision sera adaptée à ce nouveau format, il est nécessaire de permettre aux équipements plus anciens de décoder et afficher une version du même contenu dont la dynamique a été préalablement réduite par un procédé appelé ''tone mapping''. Cette thèse vise à explorer les schémas de compression HDR rétro-compatibles. Dans une première approche, un algorithme de tone mapping spécifié par l'encodeur est appliqué à l'image HDR. L'image générée, alors appelée LDR (pour ''Low Dynamic Range''), peut être encodée et décodée dans un format classique. L'encodeur transmet par ailleurs une quantité réduite d'information permettant à un décodeur HDR d'inverser l'opération de tone mapping et de reconstruire une version HDR. L'étude de ces schémas est axée sur la définition de méthodes de tone mapping optimisées pour les performances de compression. La suite de la thèse se concentre sur l'approche scalable dans laquelle les deux versions sont fournies à l'encodeur sans connaissance à priori sur l'opérateur de tone mapping utilisé. Le producteur garde donc le contrôle sur la création du contenu LDR. Cette version LDR est d'abord compressée comme une première couche. L'image reconstruite est utilisée par le codeur scalable pour compresser plus efficacement la couche HDR grâce à un mécanisme de prédiction inter-couches. Notre approche locale et non linéaire nous permet d'améliorer les performances de codage par rapport aux méthodes scalables existantes, en particulier dans le cas où un tone mapping complexe est utilisé pour générer la version LDR. / In recent years, the display technologies have been rapidly evolving. From 3D television to Ultra High Definition, the trend is now towards High Dynamic Range (HDR) displays that can reproduce a luminance range far beyond the capabilities of conventional displays. The emergence of this technology involves new standardization effort in the field of video compression. In terms of large scale content distribution, the question of backward compatibility is critical. While the future generation of television displays will be adapted to this new format, it is necessary to enable the older equipment to decode and display a version of the same content whose dynamic range has been previously reduced by a process called “tone mapping”. This thesis aims at exploring the backward compatible HDR compression schemes. In a first approach, a tone mapping operator specified by the encoder is applied to the HDR image. The resulting image, called Low Dynamic Range (LDR), can then be encoded and decoded in a conventional format. The encoder additionally transmits a small amount of information enabling a HDR capable decoder to inverse the tone mapping operator and retrieve the HDR version. The study of these schemes is directed towards the definition of tone mapping operators optimized for the compression performance. We then focus on scalable approaches, where both versions are given to the encoder without prior knowledge on the tone mapping operator used. The producer thus keeps full control on the LDR content creation process. This LDR version is compressed as a first layer. The reconstructed image is used by the scalable encoder to compress the HDR layer efficiently by performing inter-layer predictions. Thanks to a local and non-linear approach, the proposed schemes improve the coding performance compared to the existing scalable methods, especially in the case where a complex tone mapping is used for generating the LDR version.

Vision has always been one of the most important cognitive tools of human beings. In this regard, the development of image sensors opens up the potential to view objects that our eyes cannot see. One of the most promising capability in some image sensors is their single-photon sensitivity that provides information at the ultimate fundamental limit of light. Time-resolved single-photon avalanche diode (SPAD) image sensors bring a new dimension as they measure the arrival time of incident photons with a precision in the order of hundred picoseconds. In addition to this characteristic, they can be fabricated in complementary metal-oxide-semiconductor (CMOS) technology enabling the integration of complex signal processing blocks at the pixel level. These unique features made CMOS SPAD sensors a prime candidate for a broad spectrum of applications. This thesis is dedicated to the optimization and characterization of quantum imagers based on the SPADs as part of the E.U. funded SUPERTWIN project to surpass the fundamental diffraction limit known as the Rayleigh limit by exploiting the spatio-temporal correlation of entangled photons. The first characterized sensor is a 32×32-pixel SPAD array, named “SuperEllen”, with in-pixel time-to-digital converters (TDC) that measure the spatial cross-correlation functions of a flux of entangled photons. Each pixel features 19.48% fill-factor (FF) in 44.64-μm pitch fabricated in a 150-nm CMOS standard technology. The sensor is fully characterized in several electro-optical experiments, in order to be used in quantum imaging measurements. Moreover, the chip is calibrated in terms of coincidence detection achieving the minimal coincidence window determined by the SPAD jitter. The second developed sensor in the context of SUPERTWIN project is a 224×272-pixel SPAD-based array called “SuperAlice”, a multi-functional image sensor fabricated in a 110-nm CMOS image sensor technology. SuperAlice can operate in multiple modes (time-resolving or photon counting or binary imaging mode). Thanks to the digital intrinsic nature of SPAD imagers, they have an inherent capability to achieve a high frame rate. However, running at high frame rate means high I/O power consumption and thus inefficient handling of the generated data, as SPAD arrays are employed for low light applications in which data are very sparse over time and space. Here, we present three zero-suppression mechanisms to increase the frame rate without adversely affecting power consumption. A row-skipping mechanism that is implemented in both SuperEllen and SuperAlice detects the absence of SPAD activity in a row to increase the duty cycle. A current-based mechanism implemented in SuperEllen ignores reading out a full frame when the number of triggered pixels is less than a user-defined value. A different zero-suppression technique is developed in the SuperAlice chip that is based on jumping through the non-zero pixels within one row. The acquisition of TDC-based SPAD imagers can be speeded up further by storing and processing events inside the chip without the need to read out all data. An on-chip histogramming architecture based on analog counters is developed in a 150-nm CMOS standard technology. The test structure is a 16-bin histogram with 9 bit depth for each bin. SPAD technology demonstrates its capability in other applications such as automotive that demands high dynamic range (HDR) imaging. We proposed two methods based on processing photon arrival times to create HDR images. The proposed methods are validated experimentally with SuperEllen obtaining >130 dB dynamic range within 30 ms of integration time and can be further extended by using a timestamping mechanism with a higher resolution.

<p>Imaging and computer vision systems deployed in real-world environments face the challenge of accommodating a wide range of lighting conditions. However, the cost, the demand for high resolution, and the miniaturization of imaging devices impose physical constraints on sensor design, limiting both the dynamic range and effective aperture size of each pixel. Consequently, conventional CMOS sensors fail to deliver satisfactory capture in high dynamic range scenes or under photon-limited conditions, thereby impacting the performance of downstream vision tasks. In this thesis, we address two key problems: 1) exploring the utilization of spatial multiplexing, specifically spatially varying exposure tiling, to extend sensor dynamic range and optimize scene capture, and 2) developing techniques to enhance the robustness of object detection systems under photon-limited conditions.</p> <p><br></p> <p>In addition to challenges imposed by natural environments, real-world vision systems are susceptible to adversarial attacks in the form of artificially added digital content. Therefore, this thesis presents a comprehensive pipeline for constructing a robust and scalable system to counter such attacks.</p>

This thesis concerns with the introduction to the problematics of images with high dynamic range (HDR) and possibilities of HDR images compression options for display on devices with a low dynamic range (LDR). In the introduction is described historical evolution of recording of reality. It is focusing towards point of view of physics, human visual perception and digital recording. There are described the ways of generating and holding of HDR images. The thesis is corncerned to the techniques of HDR compression, it means the tone-mapping. The different techniques of tone-mapping are explained and specific aproach is targeted to the gradient domain high dynamic range compresion.