Le rendu photo-réaliste traditionnel exige un effort manuel et des calculs intensifs pour créer des scènes et rendre des images réalistes. C'est principalement pour cette raison que la création de contenus pour l’imagerie numérique de haute qualité a été limitée aux experts et le rendu hautement réaliste nécessite encore des temps de calcul significatifs. Le rendu basé image (IBR) est une alternative qui a le potentiel de rendre les applications de création et de rendu de contenus de haute qualité accessibles aux utilisateurs occasionnels, puisqu'ils peuvent générer des images photo-réalistes de haute qualité sans subir les limitations mentionnées ci-dessus. Nous avons identifié trois limitations importantes des méthodes actuelles de rendu basé image : premièrement, chaque algorithme possède des forces et faiblesses différentes, en fonction de la qualité de la reconstruction 3D et du contenu de la scène, et un seul algorithme ne permet souvent pas d’obtenir la meilleure qualité de rendu partout dans l’image. Deuxièmement, ces algorithmes présentent de forts artefacts lors du rendu d’objets manquants ou partiellement reconstruits. Troisièmement, la plupart des méthodes souffrent encore d'artefacts visuels significatifs dans les régions de l’image où la reconstruction est de faible qualité. Dans l'ensemble, cette thèse propose plusieurs améliorations significatives du rendu basé image aussi bien en termes de vitesse de rendu que de qualité d’image. Ces nouvelles solutions sont basées sur le rendu sélectif, la substitution de modèle basé sur l'apprentissage, et la prédiction et la correction des erreurs de profondeur. / Traditional photo-realistic rendering requires intensive manual and computational effort to create scenes and render realistic images. Thus, creation of content for high quality digital imagery has been limited to experts and highly realistic rendering still requires significant computational time. Image-Based Rendering (IBR) is an alternative which has the potential of making high-quality content creation and rendering applications accessible to casual users, since they can generate high quality photo-realistic imagery without the limitations mentioned above. We identified three important shortcomings of current IBR methods: First, each algorithm has different strengths and weaknesses, depending on 3D reconstruction quality and scene content and often no single algorithm offers the best image quality everywhere in the image. Second, such algorithms present strong artifacts when rendering partially reconstructed objects or missing objects. Third, most methods still result in significant visual artifacts in image regions where reconstruction is poor. Overall, this thesis addresses significant shortcomings of IBR for both speed and image quality, offering novel and effective solutions based on selective rendering, learning-based model substitution and depth error prediction and correction.

The evolution to 5G and its use cases is driven by data-intensive applications requiring higher data rates over wireless channels. This has led to research in massive multiple input multiple output (MIMO) techniques and the use of the millimeter wave (mm wave) band. Because of the higher path loss at mm wave frequencies and the poor scattering nature of the mm wave channel (fewer paths exist), this thesis first proposes the use of the sphere decoding (SD) algorithm, and the semidefinite relaxation (SDR) detector to improve the performance of a uniform planar array (UPA) hybrid beamforming technique with large antenna arrays. The second contributions of this thesis consist of a low-complexity algorithm using the gradient descent for hybrid precoding and combining designs in mm wave systems. Also, in this thesis we present a low-complexity algorithm for hybrid precoding and combining designs that uses momentum gradient descent and Newton’s Method for mm wave systems which makes the objective function converge faster compared to other iterative methods in the literature; the two proposed low-complexity algorithms for hybrid precoding and combining do not depend on the antenna array geometry, unlike the orthogonal matching pursuit (OMP) hybrid precoding/combining approach. Moreover, these algorithms allow hybrid precoders/combiners to yield a performance very close to that of the optimal unconstrained digital precoders and combiners with a small number of iterations. Simulation results verify that the proposed hybrid precoding/combining scheme that uses momentum gradient descent and Newton’s Method outperforms previous methods that appear in the literature in terms of bit error rate (BER) and achievable spectral efficiency with lower complexity. Finally, an iterative algorithm that directly converts the hybrid precoding/combining in the full array (FA) architecture to subarray (SA) architecture is proposed and examined in this thesis. It is called direct conversion of iterative hybrid precoding/combining from FA to SA (DCIFS) hybrid precoding/combining. The proposed DCIFS design takes into consideration the matrix structure of the analog and baseband precoding and combining in the design derivation. Moreover, it does not depend on the antenna array geometry, unlike other techniques, such as the orthogonal matching pursuit (OMP) hybrid precoding/combining approach, nor does it assume any other constraints. Simulation results show that the proposed DCIFS hybrid design, when compared to the FA hybrid designs counterpart, can provide a spectral efficiency that is close to optimum while maintaining a very low complexity and better spectral efficiency than the conventional SA hybrid design with the same hardware complexity.