Improved Nonlinear Transform Source-Channel Coding to Catalyze Semantic Communications

Recent deep learning methods have led to increased interest in solving high-efficiency end-to-end transmission problems. These methods, we call nonlinear transform source-channel coding (NTSCC), extract the semantic latent features of source signal, and learn entropy model to guide the joint source-channel coding with variable rate to transmit latent features over wireless channels. In this paper, we propose a comprehensive framework for improving NTSCC, thereby higher system coding gain, better model versatility, and more flexible adaptation strategy aligned with semantic guidance are all achieved. This new sophisticated NTSCC model is now ready to support large-size data interaction in emerging XR, which catalyzes the application of semantic communications. Specifically, we propose three useful improvement approaches. First, we introduce a contextual entropy model to better capture the spatial correlations among the semantic latent features, thereby more accurate rate allocation and contextual joint source-channel coding are developed accordingly to enable higher coding gain. On that basis, we further propose response network architectures to formulate versatile NTSCC, i.e., once-trained model supports various rates and channel states that benefits the practical deployment. Following this, we propose an online latent feature editing method to enable more flexible coding rate control aligned with some specific semantic guidance. By comprehensively applying the above three improvement methods for NTSCC, a deployment-friendly semantic coded transmission system stands out finally. Our improved NTSCC system has been experimentally verified to achieve considerable bandwidth saving versus the state-of-the-art engineered VTM + 5G LDPC coded transmission system with lower processing latency

JOINT CODING OF MULTIMODAL BIOMEDICAL IMAGES US ING CONVOLUTIONAL NEURAL NETWORKS

The massive volume of data generated daily by the gathering of medical images with different modalities might be difficult to store in medical facilities and share through communication networks. To alleviate this issue, efficient compression methods must be implemented to reduce the amount of storage and transmission resources required in such applications. However, since the preservation of all image details is highly important in the medical context, the use of lossless image compression algorithms is of utmost importance. This thesis presents the research results on a lossless compression scheme designed to encode both computerized tomography (CT) and positron emission tomography (PET). Different techniques, such as image-to-image translation, intra prediction, and inter prediction are used. Redundancies between both image modalities are also investigated. To perform the image-to-image translation approach, we resort to lossless compression of the original CT data and apply a cross-modality image translation generative adversarial network to obtain an estimation of the corresponding PET. Two approaches were implemented and evaluated to determine a PET residue that will be compressed along with the original CT. In the first method, the residue resulting from the differences between the original PET and its estimation is encoded, whereas in the second method, the residue is obtained using encoders inter-prediction coding tools. Thus, in alternative to compressing two independent picture modalities, i.e., both images of the original PET-CT pair solely the CT is independently encoded alongside with the PET residue, in the proposed method. Along with the proposed pipeline, a post-processing optimization algorithm that modifies the estimated PET image by altering the contrast and rescaling the image is implemented to maximize the compression efficiency. Four different versions (subsets) of a publicly available PET-CT pair dataset were tested. The first proposed subset was used to demonstrate that the concept developed in this work is capable of surpassing the traditional compression schemes. The obtained results showed gains of up to 8.9% using the HEVC. On the other side, JPEG2k proved not to be the most suitable as it failed to obtain good results, having reached only -9.1% compression gain. For the remaining (more challenging) subsets, the results reveal that the proposed refined post-processing scheme attains, when compared to conventional compression methods, up 6.33% compression gain using HEVC, and 7.78% using VVC

Nouvelles méthodes de prédiction inter-images pour la compression d’images et de vidéos

Due to the large availability of video cameras and new social media practices, as well as the emergence of cloud services, images and videosconstitute today a significant amount of the total data that is transmitted over the internet. Video streaming applications account for more than 70% of the world internet bandwidth. Whereas billions of images are already stored in the cloud and millions are uploaded every day. The ever growing streaming and storage requirements of these media require the constant improvements of image and video coding tools. This thesis aims at exploring novel approaches for improving current inter-prediction methods. Such methods leverage redundancies between similar frames, and were originally developed in the context of video compression. In a first approach, novel global and local inter-prediction tools are associated to improve the efficiency of image sets compression schemes based on video codecs. By leveraging a global geometric and photometric compensation with a locally linear prediction, significant improvements can be obtained. A second approach is then proposed which introduces a region-based inter-prediction scheme. The proposed method is able to improve the coding performances compared to existing solutions by estimating and compensating geometric and photometric distortions on a semi-local level. This approach is then adapted and validated in the context of video compression. Bit-rate improvements are obtained, especially for sequences displaying complex real-world motions such as zooms and rotations. The last part of the thesis focuses on deep learning approaches for inter-prediction. Deep neural networks have shown striking results for a large number of computer vision tasks over the last years. Deep learning based methods proposed for frame interpolation applications are studied here in the context of video compression. Coding performance improvements over traditional motion estimation and compensation methods highlight the potential of these deep architectures.En raison de la grande disponibilité des dispositifs de capture vidéo et des nouvelles pratiques liées aux réseaux sociaux, ainsi qu’à l’émergence desservices en ligne, les images et les vidéos constituent aujourd’hui une partie importante de données transmises sur internet. Les applications de streaming vidéo représentent ainsi plus de 70% de la bande passante totale de l’internet. Des milliards d’images sont déjà stockées dans le cloud et des millions y sont téléchargés chaque jour. Les besoins toujours croissants en streaming et stockage nécessitent donc une amélioration constante des outils de compression d’image et de vidéo. Cette thèse vise à explorer des nouvelles approches pour améliorer les méthodes actuelles de prédiction inter-images. De telles méthodes tirent parti des redondances entre images similaires, et ont été développées à l’origine dans le contexte de la vidéo compression. Dans une première partie, de nouveaux outils de prédiction inter globaux et locaux sont associés pour améliorer l’efficacité des schémas de compression de bases de données d’image. En associant une compensation géométrique et photométrique globale avec une prédiction linéaire locale, des améliorations significatives peuvent être obtenues. Une seconde approche est ensuite proposée qui introduit un schéma deprédiction inter par régions. La méthode proposée est en mesure d’améliorer les performances de codage par rapport aux solutions existantes en estimant et en compensant les distorsions géométriques et photométriques à une échelle semi locale. Cette approche est ensuite adaptée et validée dans le cadre de la compression vidéo. Des améliorations en réduction de débit sont obtenues, en particulier pour les séquences présentant des mouvements complexes réels tels que des zooms et des rotations. La dernière partie de la thèse se concentre sur l’étude des méthodes d’apprentissage en profondeur dans le cadre de la prédiction inter. Ces dernières années, les réseaux de neurones profonds ont obtenu des résultats impressionnants pour un grand nombre de tâches de vision par ordinateur. Les méthodes basées sur l’apprentissage en profondeur proposéesà l’origine pour de l’interpolation d’images sont étudiées ici dans le contexte de la compression vidéo. Des améliorations en terme de performances de codage sont obtenues par rapport aux méthodes d’estimation et de compensation de mouvements traditionnelles. Ces résultats mettent en évidence le fort potentiel de ces architectures profondes dans le domaine de la compression vidéo

Die Virtuelle Videokamera: ein System zur Blickpunktsynthese in beliebigen, dynamischen Szenen

The Virtual Video Camera project strives to create free viewpoint video from casually captured multi-view data. Multiple video streams of a dynamic scene are captured with off-the-shelf camcorders, and the user can re-render the scene from novel perspectives. In this thesis the algorithmic core of the Virtual Video Camera is presented. This includes the algorithm for image correspondence estimation as well as the image-based renderer. Furthermore, its application in the context of an actual video production is showcased, and the rendering and image processing pipeline is extended to incorporate depth information.Das Virtual Video Camera Projekt dient der Erzeugung von Free Viewpoint Video Ansichten von Multi-View Aufnahmen: Material mehrerer Videoströme wird hierzu mit handelsüblichen Camcordern aufgezeichnet. Im Anschluss kann die Szene aus beliebigen, von den ursprünglichen Kameras nicht abgedeckten Blickwinkeln betrachtet werden. In dieser Dissertation wird der algorithmische Kern der Virtual Video Camera vorgestellt. Dies beinhaltet das Verfahren zur Bildkorrespondenzschätzung sowie den bildbasierten Renderer. Darüber hinaus wird die Anwendung im Kontext einer Videoproduktion beleuchtet. Dazu wird die bildbasierte Erzeugung neuer Blickpunkte um die Erzeugung und Einbindung von Tiefeninformationen erweitert

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Neural Extended Kalman Filter for State Estimation of Automated Guided Vehicle in Manufacturing Environment

To navigate autonomously in a manufacturing environment Automated Guided Vehicle (AGV) needs the ability to infer its pose. This paper presents the implementation of the Extended Kalman Filter (EKF) coupled with a feedforward neural network for the Visual Simultaneous Localization and Mapping (VSLAM). The neural extended Kalman filter (NEKF) is applied on-line to model error between real and estimated robot motion. Implementation of the NEKF is achieved by using mobile robot, an experimental environment and a simple camera. By introducing neural network into the EKF estimation procedure, the quality of performance can be improved

Prediction of Robot Execution Failures Using Neural Networks

In recent years, the industrial robotic systems are designed with abilities to adapt and to learn in a structured or unstructured environment. They are able to predict and to react to the undesirable and uncontrollable disturbances which frequently interfere in mission accomplishment. In order to prevent system failure and/or unwanted robot behaviour, various techniques have been addressed. In this study, a novel approach based on the neural networks (NNs) is employed for prediction of robot execution failures. The training and testing dataset used in the experiment consists of forces and torques memorized immediately after the real robot failed in assignment execution. Two types of networks are utilized in order to find best prediction method - recurrent NNs and feedforward NNs. Moreover, we investigated 24 neural architectures implemented in Matlab software package. The experimental results confirm that this approach can be successfully applied to the failures prediction problem, and that the NNs outperform other artificial intelligence techniques in this domain. To further validate a novel method, real world experiments are conducted on a Khepera II mobile robot in an indoor structured environment. The obtained results for trajectory tracking problem proved usefulness and the applicability of the proposed solution