Lossless compression of images with specific characteristics

Doutoramento em Engenharia ElectrotécnicaA compressão de certos tipos de imagens é um desafio para algumas normas de compressão de imagem. Esta tese investiga a compressão sem perdas de imagens com características especiais, em particular imagens simples, imagens de cor indexada e imagens de microarrays. Estamos interessados no desenvolvimento de métodos de compressão completos e no estudo de técnicas de pré-processamento que possam ser utilizadas em conjunto com as normas de compressão de imagem. A esparsidade do histograma, uma propriedade das imagens simples, é um dos assuntos abordados nesta tese. Desenvolvemos uma técnica de pré-processamento, denominada compactação de histogramas, que explora esta propriedade e que pode ser usada em conjunto com as normas de compressão de imagem para um melhoramento significativo da eficiência de compressão. A compactação de histogramas e os algoritmos de reordenação podem ser usados como préprocessamento para melhorar a compressão sem perdas de imagens de cor indexada. Esta tese apresenta vários algoritmos e um estudo abrangente dos métodos já existentes. Métodos específicos, como é o caso da decomposição em árvores binárias, são também estudados e propostos. O uso de microarrays em biologia encontra-se em franca expansão. Devido ao elevado volume de dados gerados por experiência, são necessárias técnicas de compressão sem perdas. Nesta tese, exploramos a utilização de normas de compressão sem perdas e apresentamos novos algoritmos para codificar eficientemente este tipo de imagens, baseados em modelos de contexto finito e codificação aritmética.The compression of some types of images is a challenge for some standard compression techniques. This thesis investigates the lossless compression of images with specific characteristics, namely simple images, color-indexed images and microarray images. We are interested in the development of complete compression methods and in the study of preprocessing algorithms that could be used together with standard compression methods. The histogram sparseness, a property of simple images, is addressed in this thesis. We developed a preprocessing technique, denoted histogram packing, that explores this property and can be used with standard compression methods for improving significantly their efficiency. Histogram packing and palette reordering algorithms can be used as a preprocessing step for improving the lossless compression of color-indexed images. This thesis presents several algorithms and a comprehensive study of the already existing methods. Specific compression methods, such as binary tree decomposition, are also addressed. The use of microarray expression data in state-of-the-art biology has been well established and due to the significant volume of data generated per experiment, efficient lossless compression methods are needed. In this thesis, we explore the use of standard image coding techniques and we present new algorithms to efficiently compress this type of images, based on finite-context modeling and arithmetic coding

Point Process Models for Heterogeneous Event Time Data

Interaction event times observed on a social network provide valuable information for social scientists to gain insight into complex social dynamics that are challenging to understand. However, it can be difficult to accurately represent the heterogeneity in the data and to model the dependence structure in the network system. This requires flexible models that can capture the complicated dynamics and complex patterns. Point process models offer an elegant framework for modeling event time data. This dissertation concentrates on developing point process models and related diagnostic tools, with a real data application involving an animal behavior network. In this dissertation, we first propose a Markov-modulated Hawkes process (MMHP) model to capture the sporadic and bursty patterns often observed in event time data. A Bayesian inference procedure is developed to evaluate the likelihood by using a variational approximation and the forward-backward algorithm. The validity of the proposed model and associated estimation algorithms is demonstrated using synthetic data and the animal behavior data. Facilitated by the power of the MMHP model, we construct network point process models that can capture a social hierarchy structure by embedding nodes in a latent space that can represent the underlying social ranks. Our model provides a ranking method for social hierarchy studies and describes the dynamics of social hierarchy formation from a novel perspective – taking advantage of the detailed information available in event time data. We show that the network point process models appropriately captures the temporal dynamics and heterogeneity in the network event time data, by providing meaningful inferred rankings and by calibrating the accuracy of predictions with relevant measures of uncertainty. In addition to developing a sensible and flexible model for network event time data, the last part of this dissertation provides essential tools for diagnosing lack of fit issues for such models. We develop a systematic set of diagnostic tools and visualizations for point process models fitted to data in the dynamic network setting. By inspecting the structure of the residual process and Pearson residual on the network, we can validate whether a model adequately captures the temporal and network dependence structures in the observed data

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Técnicas de compresión de imágenes hiperespectrales sobre hardware reconfigurable

Tesis de la Universidad Complutense de Madrid, Facultad de Informática, leída el 18-12-2020Sensors are nowadays in all aspects of human life. When possible, sensors are used remotely. This is less intrusive, avoids interferces in the measuring process, and more convenient for the scientist. One of the most recurrent concerns in the last decades has been sustainability of the planet, and how the changes it is facing can be monitored. Remote sensing of the earth has seen an explosion in activity, with satellites now being launched on a weekly basis to perform remote analysis of the earth, and planes surveying vast areas for closer analysis...Los sensores aparecen hoy en día en todos los aspectos de nuestra vida. Cuando es posible, de manera remota. Esto es menos intrusivo, evita interferencias en el proceso de medida, y además facilita el trabajo científico. Una de las preocupaciones recurrentes en las últimas décadas ha sido la sotenibilidad del planeta, y cómo menitoirzar los cambios a los que se enfrenta. Los estudios remotos de la tierra han visto un gran crecimiento, con satélites lanzados semanalmente para analizar la superficie, y aviones sobrevolando grades áreas para análisis más precisos...Fac. de InformáticaTRUEunpu

Media aesthetics based multimedia storytelling.

Since the earliest of times, humans have been interested in recording their life experiences, for future reference and for storytelling purposes. This task of recording experiences --i.e., both image and video capture-- has never before in history been as easy as it is today. This is creating a digital information overload that is becoming a great concern for the people that are trying to preserve their life experiences. As high-resolution digital still and video cameras become increasingly pervasive, unprecedented amounts of multimedia, are being downloaded to personal hard drives, and also uploaded to online social networks on a daily basis. The work presented in this dissertation is a contribution in the area of multimedia organization, as well as automatic selection of media for storytelling purposes, which eases the human task of summarizing a collection of images or videos in order to be shared with other people. As opposed to some prior art in this area, we have taken an approach in which neither user generated tags nor comments --that describe the photographs, either in their local or on-line repositories-- are taken into account, and also no user interaction with the algorithms is expected. We take an image analysis approach where both the context images --e.g. images from online social networks to which the image stories are going to be uploaded--, and the collection images --i.e., the collection of images or videos that needs to be summarized into a story--, are analyzed using image processing algorithms. This allows us to extract relevant metadata that can be used in the summarization process. Multimedia-storytellers usually follow three main steps when preparing their stories: first they choose the main story characters, the main events to describe, and finally from these media sub-groups, they choose the media based on their relevance to the story as well as based on their aesthetic value. Therefore, one of the main contributions of our work has been the design of computational models --both regression based, as well as classification based-- that correlate well with human perception of the aesthetic value of images and videos. These computational aesthetics models have been integrated into automatic selection algorithms for multimedia storytelling, which are another important contribution of our work. A human centric approach has been used in all experiments where it was feasible, and also in order to assess the final summarization results, i.e., humans are always the final judges of our algorithms, either by inspecting the aesthetic quality of the media, or by inspecting the final story generated by our algorithms. We are aware that a perfect automatically generated story summary is very hard to obtain, given the many subjective factors that play a role in such a creative process; rather, the presented approach should be seen as a first step in the storytelling creative process which removes some of the ground work that would be tedious and time consuming for the user. Overall, the main contributions of this work can be capitalized in three: (1) new media aesthetics models for both images and videos that correlate with human perception, (2) new scalable multimedia collection structures that ease the process of media summarization, and finally, (3) new media selection algorithms that are optimized for multimedia storytelling purposes.Postprint (published version

[Activity of Institute for Computer Applications in Science and Engineering]

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, fluid mechanics, and computer science

Exclusive-or preprocessing and dictionary coding of continuous-tone images.

The field of lossless image compression studies the various ways to represent image data in the most compact and efficient manner possible that also allows the image to be reproduced without any loss. One of the most efficient strategies used in lossless compression is to introduce entropy reduction through decorrelation. This study focuses on using the exclusive-or logic operator in a decorrelation filter as the preprocessing phase of lossless image compression of continuous-tone images. The exclusive-or logic operator is simply and reversibly applied to continuous-tone images for the purpose of extracting differences between neighboring pixels. Implementation of the exclusive-or operator also does not introduce data expansion. Traditional as well as innovative prediction methods are included for the creation of inputs for the exclusive-or logic based decorrelation filter. The results of the filter are then encoded by a variation of the Lempel-Ziv-Welch dictionary coder. Dictionary coding is selected for the coding phase of the algorithm because it does not require the storage of code tables or probabilities and because it is lower in complexity than other popular options such as Huffman or Arithmetic coding. The first modification of the Lempel-Ziv-Welch dictionary coder is that image data can be read in a sequence that is linear, 2-dimensional, or an adaptive combination of both. The second modification of the dictionary coder is that the coder can instead include multiple, dynamically chosen dictionaries. Experiments indicate that the exclusive-or operator based decorrelation filter when combined with a modified Lempel-Ziv-Welch dictionary coder provides compression comparable to algorithms that represent the current standard in lossless compression. The proposed algorithm provides compression performance that is below the Context-Based, Adaptive, Lossless Image Compression (CALIC) algorithm by 23%, below the Low Complexity Lossless Compression for Images (LOCO-I) algorithm by 19%, and below the Portable Network Graphics implementation of the Deflate algorithm by 7%, but above the Zip implementation of the Deflate algorithm by 24%. The proposed algorithm uses the exclusive-or operator in the modeling phase and uses modified Lempel-Ziv-Welch dictionary coding in the coding phase to form a low complexity, reversible, and dynamic method of lossless image compression

Very low bit rate parametric audio coding

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