Efficient HEVC-based video adaptation using transcoding

In a video transmission system, it is important to take into account the great diversity of the network/end-user constraints. On the one hand, video content is typically streamed over a network that is characterized by different bandwidth capacities. In many cases, the bandwidth is insufficient to transfer the video at its original quality. On the other hand, a single video is often played by multiple devices like PCs, laptops, and cell phones. Obviously, a single video would not satisfy their different constraints. These diversities of the network and devices capacity lead to the need for video adaptation techniques, e.g., a reduction of the bit rate or spatial resolution. Video transcoding, which modifies a property of the video without the change of the coding format, has been well-known as an efficient adaptation solution. However, this approach comes along with a high computational complexity, resulting in huge energy consumption in the network and possibly network latency. This presentation provides several optimization strategies for the transcoding process of HEVC (the latest High Efficiency Video Coding standard) video streams. First, the computational complexity of a bit rate transcoder (transrater) is reduced. We proposed several techniques to speed-up the encoder of a transrater, notably a machine-learning-based approach and a novel coding-mode evaluation strategy have been proposed. Moreover, the motion estimation process of the encoder has been optimized with the use of decision theory and the proposed fast search patterns. Second, the issues and challenges of a spatial transcoder have been solved by using machine-learning algorithms. Thanks to their great performance, the proposed techniques are expected to significantly help HEVC gain popularity in a wide range of modern multimedia applications

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

Improved K-means clustering algorithms : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science, Massey University, New Zealand

K-means clustering algorithm is designed to divide the samples into subsets with the goal that maximizes the intra-subset similarity and inter-subset dissimilarity where the similarity measures the relationship between two samples. As an unsupervised learning technique, K-means clustering algorithm is considered one of the most used clustering algorithms and has been applied in a variety of areas such as artificial intelligence, data mining, biology, psychology, marketing, medicine, etc. K-means clustering algorithm is not robust and its clustering result depends on the initialization, the similarity measure, and the predefined cluster number. Previous research focused on solving a part of these issues but has not focused on solving them in a unified framework. However, fixing one of these issues does not guarantee the best performance. To improve K-means clustering algorithm, one of the most famous and widely used clustering algorithms, by solving its issues simultaneously is challenging and significant. This thesis conducts an extensive research on K-means clustering algorithm aiming to improve it. First, we propose the Initialization-Similarity (IS) clustering algorithm to solve the issues of the initialization and the similarity measure of K-means clustering algorithm in a unified way. Specifically, we propose to fix the initialization of the clustering by using sum-of-norms (SON) which outputs the new representation of the original samples and to learn the similarity matrix based on the data distribution. Furthermore, the derived new representation is used to conduct K-means clustering. Second, we propose a Joint Feature Selection with Dynamic Spectral (FSDS) clustering algorithm to solve the issues of the cluster number determination, the similarity measure, and the robustness of the clustering by selecting effective features and reducing the influence of outliers simultaneously. Specifically, we propose to learn the similarity matrix based on the data distribution as well as adding the ranked constraint on the Laplacian matrix of the learned similarity matrix to automatically output the cluster number. Furthermore, the proposed algorithm employs the L2,1-norm as the sparse constraints on the regularization term and the loss function to remove the redundant features and reduce the influence of outliers respectively. Third, we propose a Joint Robust Multi-view (JRM) spectral clustering algorithm that conducts clustering for multi-view data while solving the initialization issue, the cluster number determination, the similarity measure learning, the removal of the redundant features, and the reduction of outlier influence in a unified way. Finally, the proposed algorithms outperformed the state-of-the-art clustering algorithms on real data sets. Moreover, we theoretically prove the convergences of the proposed optimization methods for the proposed objective functions

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Молодежь и современные информационные технологии : сборник трудов XI Международной научно-практической конференции студентов, аспирантов и молодых ученых, г. Томск, 13-16 ноября 2013 г.

Сборник содержит доклады, представленные на XI Международной научно-практическую конференцию студентов, аспирантов и молодых ученых "Молодежь и современные информационные технологии", прошедшей в Томском политехническом университете на базе института Кибернетики. Материалы сбор-ника отражают доклады студентов, аспирантов и молодых ученых, принятые к обсуждению на секциях: "Микропроцессорные системы, компьютерные сети и телекоммуникации", "Математическое моделирование и компьютерный анализ данных", "Информационные и интеллектуальные системы (в прикладных областях)", "Автоматизация и управление в технических системах", "Информационные и программные системы в производстве и управлении", "Геоинформационные системы и технологии", "Компьютерные измерительные системы и метрология", "Компьютерная графика и дизайн", "Информационные технологии в машиностроении", "Информационные технологии в гуманитарных и медицинских исследованиях". Сборник предназначен для специалистов в области информационных технологий, студентов и аспирантов соответствующих специальносте

To understand, model and design an e-mobility system in its urban context

The electric vehicles (EVs) are emerging as an alternative solution to the conventional gasoline vehicles. The EV market faces different issues related to limited range, which are associated with the battery technology and the charging network. A clear emphasis is placed on how well the supporting recharging facilities (RFs) are deployed in order to reduce the limited range. The aim of this study is to investigate how suitably the locations for RFs can be chosen in order to satisfy the demand. Charging demand is a multifaceted problem, the majority of them charge at home and do not experience the maximum range of the EV in an attempt to avoid being stranded with a flat battery, and the deployment of rapid chargers is costly. A desired balance between supply and demand can be achieved by identifying the most influential factors affecting the design and use of the RFs. The fundamental monitoring of the use of RFs would reflect the quality of design, highlight the emerging design needs, and assist with the strategic deployment of the RFs. The interest in alternative transport is shaped primarily by consumer perceptions and users’ feedback. This thesis integrates visual and statistical elements in order to understand the end-­‐user emerging design needs and to model the RFs. In this thesis, over 12,725 charging events were analysed in conjunction to 20 interviews with EV users and stakeholders. With the use of an agent-­‐based modelling technique, it has been possible to capture and simulate the electric-­‐mobility system. By means of integrated spatiotemporal modelling, the results indicated that the proposed model is capable of identifying candidate locations for deploying RFs. A multi method approach is presented to understand the concepts of, model and design the RFs. The outcome of this research should be of interest to planning authorities and policy makers of alternative means of transport