Motion Scalability for Video Coding with Flexible Spatio-Temporal Decompositions

PhDThe research presented in this thesis aims to extend the scalability range of the wavelet-based video coding systems in order to achieve fully scalable coding with a wide range of available decoding points. Since the temporal redundancy regularly comprises the main portion of the global video sequence redundancy, the techniques that can be generally termed motion decorrelation techniques have a central role in the overall compression performance. For this reason the scalable motion modelling and coding are of utmost importance, and specifically, in this thesis possible solutions are identified and analysed. The main contributions of the presented research are grouped into two interrelated and complementary topics. Firstly a flexible motion model with rateoptimised estimation technique is introduced. The proposed motion model is based on tree structures and allows high adaptability needed for layered motion coding. The flexible structure for motion compensation allows for optimisation at different stages of the adaptive spatio-temporal decomposition, which is crucial for scalable coding that targets decoding on different resolutions. By utilising an adaptive choice of wavelet filterbank, the model enables high compression based on efficient mode selection. Secondly, solutions for scalable motion modelling and coding are developed. These solutions are based on precision limiting of motion vectors and creation of a layered motion structure that describes hierarchically coded motion. The solution based on precision limiting relies on layered bit-plane coding of motion vector values. The second solution builds on recently established techniques that impose scalability on a motion structure. The new approach is based on two major improvements: the evaluation of distortion in temporal Subbands and motion search in temporal subbands that finds the optimal motion vectors for layered motion structure. Exhaustive tests on the rate-distortion performance in demanding scalable video coding scenarios show benefits of application of both developed flexible motion model and various solutions for scalable motion coding

Fast Motion Estimation Algorithms for Block-Based Video Coding Encoders

The objective of my research is reducing the complexity of video coding standards in real-time scalable and multi-view applications

Digital Signal Processing

Contains an introduction and reports on twenty research projects.National Science Foundation (Grant ECS 84-07285)U.S. Navy - Office of Naval Research (Contract N00014-81-K-0742)National Science Foundation FellowshipSanders Associates, Inc.U.S. Air Force - Office of Scientific Research (Contract F19628-85-K-0028)Canada, Bell Northern Research ScholarshipCanada, Fonds pour la Formation de Chercheurs et l'Aide a la Recherche Postgraduate FellowshipCanada, Natural Science and Engineering Research Council Postgraduate FellowshipU.S. Navy - Office of Naval Research (Contract N00014-81-K-0472)Fanny and John Hertz Foundation FellowshipCenter for Advanced Television StudiesAmoco Foundation FellowshipU.S. Air Force - Office of Scientific Research (Contract F19628-85-K-0028

Selected topics in video coding and computer vision

Video applications ranging from multimedia communication to computer vision have been extensively studied in the past decades. However, the emergence of new applications continues to raise questions that are only partially answered by existing techniques. This thesis studies three selected topics related to video: intra prediction in block-based video coding, pedestrian detection and tracking in infrared imagery, and multi-view video alignment.;In the state-of-art video coding standard H.264/AVC, intra prediction is defined on the hierarchical quad-tree based block partitioning structure which fails to exploit the geometric constraint of edges. We propose a geometry-adaptive block partitioning structure and a new intra prediction algorithm named geometry-adaptive intra prediction (GAIP). A new texture prediction algorithm named geometry-adaptive intra displacement prediction (GAIDP) is also developed by extending the original intra displacement prediction (IDP) algorithm with the geometry-adaptive block partitions. Simulations on various test sequences demonstrate that intra coding performance of H.264/AVC can be significantly improved by incorporating the proposed geometry adaptive algorithms.;In recent years, due to the decreasing cost of thermal sensors, pedestrian detection and tracking in infrared imagery has become a topic of interest for night vision and all weather surveillance applications. We propose a novel approach for detecting and tracking pedestrians in infrared imagery based on a layered representation of infrared images. Pedestrians are detected from the foreground layer by a Principle Component Analysis (PCA) based scheme using the appearance cue. To facilitate the task of pedestrian tracking, we formulate the problem of shot segmentation and present a graph matching-based tracking algorithm. Simulations with both OSU Infrared Image Database and WVU Infrared Video Database are reported to demonstrate the accuracy and robustness of our algorithms.;Multi-view video alignment is a process to facilitate the fusion of non-synchronized multi-view video sequences for various applications including automatic video based surveillance and video metrology. In this thesis, we propose an accurate multi-view video alignment algorithm that iteratively aligns two sequences in space and time. To achieve an accurate sub-frame temporal alignment, we generalize the existing phase-correlation algorithm to 3-D case. We also present a novel method to obtain the ground-truth of the temporal alignment by using supplementary audio signals sampled at a much higher rate. The accuracy of our algorithm is verified by simulations using real-world sequences

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

Scalable coding of HDTV pictures using the MPEG coder

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (leaves 118-121).by Adnan Husain Lawai.M.S

Fast motion estimation algorithms for block-based video coding encoders

The objective of my research is reducing the complexity of video coding standards in real-time scalable and multi-view applications.EThOS - Electronic Theses Online ServiceGBUnited Kingdo