Orientadores: Yuzo Iano, Ana Lúcia Mendes Cruz Silvestre da Silva / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-18T18:35:20Z (GMT). No. of bitstreams: 1 Leite_RicardoBarroso_M.pdf: 4189561 bytes, checksum: 31cbbc85c9fa0ec77e54c0fdfac29e3f (MD5) Previous issue date: 2011 / Resumo: Os avanços em telecomunicações e o desenvolvimento dos equipamentos digitais impulsionaram diversas áreas de pesquisa relacionadas à codificação e compressão de imagens. Dentre as áreas de atuação destacam-se as aplicações para dispositivos móveis (celulares, smartphones, iPhones, iPads entre outros), que se caracterizam por baixas taxas de transmissão de dados. Entretanto, imagens codificadas com os padrões atualmente em estado-da-arte apresentam artefatos visuais característicos, como efeito de bloco e ringing. Para contornar a inabilidade das transformadas ortogonais em lidar com a geometria, é proposto na literatura o uso de dicionários wedgelet e da decomposição cartoon-textura. Nesse contexto, propõe-se um método de codificação híbrido wedgelet-wavelet inédito que preserva componentes de cartoon e textura, superando em qualidade visual ao uso de dicionários isolados e se aproximando do desempenho de sistemas de codificação completos, tais como o padrão JPEG 2000. Os ganhos de desempenho, principalmente em qualidade visual das imagens reconstruídas para baixas taxas de dados, indicam que a metodologia apresentada pode vir a ser incluída em sistemas de transmissão com restrições de largura de banda, como por exemplo a TV digital móvel / Abstract: Advances in telecommunications and the development of digital equipments have improved several research areas related to coding and image compression. Among these application fields are the mobile devices (cellphones, smartphones, iPhones, iPad, and others), characterized by low data transmission rates. However, images encoded by state-of-the-art standards present characteristic visual artifacts, like blocking and ringing effects. To surpass the disadvantages of orthogonal transforms in dealing with geometry, wedgelets dictionaries and cartoon-texture decomposition are proposed in literature. In this context, a new hybrid wedgelet-wavelet coding method that preserves cartoon and texture components is proposed, achieving better visual quality than the use of isolated dictionaries, approaching the performance of complete codification systems, such as the JPEG 2000. The performance gains, especially concerning visual quality of the reconstructed images using low data rates, show that this methodology might be adopted in restricted bandwidth transmission systems, such as the digital mobile TV / Mestrado / Telecomunicações e Telemática / Mestre em Engenharia Elétrica

Orientador: Amauri Lopes / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-07-23T22:31:34Z (GMT). No. of bitstreams: 1 Farias_MyleneChristineQueirozde_M.pdf: 12671281 bytes, checksum: 45b2da78003177cfbc994aa1c7fb4992 (MD5) Previous issue date: 1998 / Resumo: O objetivo deste trabalho foi realizar uma análise sobre as potencialidades de aplicação da transformada wavelet na compressão de imagens estáticas monocromáticas. Realizamos um estudo da teoria wavelet, com ênfase na aplicação em compressão de imagens. Realizamos também um estudo sobre as principais técnicas de compressão de imagens estáticas, detendo-nos um pouco mais no estudo dos decodificadores com-perdas. Utilizando esta bagagem, construímos alguns esquemas de compressão de imagens baseados na transformada wavelet. Através de simulações, pudemos fazer uma análise do tipo de codificador baseado na transformada wavelet mais adequado à compressão de imagens. Vislumbrando novas aplicações da transformada wavelet na área de processameno de imagens, estudamos também alguns esquemas de compressão de sinais de vídeo baseados na transformada wavelet, apresentando inclusive alguns conceitos sobre a escalonabilidade. ...Observação: O resumo, na íntegra, poderá ser visualizado no texto completo da tese digital / Abstract: The purpose of this work is to analyze the applications of the wavelet transform in the compression of static monochromatic images. We have made a study of the wavelet theory with emphasis on its applications in image compression. We have also studied some of the most important techniques used in image compression. Based on this background a few image compression schemes have been constructed and simulated in order to evaluate their performance. Taking into account the modern telecommunications scenario, we have also made a brief study of the application of the wavelet transform in video compression, including an introduction to scalability and its relation with wavelet transform. ...Note: The complete abstract is available with the full electronic digital thesis or dissertations / Mestrado / Mestre em Engenharia Elétrica

Image acquisition devices, as well as image processing theory, algorithms, and hardware have advanced to the point that low Size-Weight-and-Power, real-time embedded imaging systems have become a reality. To be practical in a fielded application, an image processing sub-system must be able to conduct multiple, often highly complex tasks, in real-time. The design and construction of such systems have to address technical challenges, including real-time, low-latency processing and fixed-point algorithms in order to leverage lowest-power computing platforms. Further design complications stem from the reality that state-of-the-art image processing algorithms take very different forms, greatly complicating low-latency implementations. This dissertation presents the design and preliminary implementation of an image processing sub-system that minimizes computational complexity and power consumption by eliminating repeated transformations between processing domains. Specifically, this processing chain utilizes the LeGall 5/3 wavelet as the basis for applying multiple algorithms within a single domain. The wavelet processing chain is compared, in terms of image quality, computational cost, and power consumption, to a benchmark processing chain comprised of algorithms intended to produce high quality image results. Image quality is assessed through a subject matter expert evaluation. Computational cost is analyzed theoretically and empirically, and the power consumption is derived from the execution times and characteristics of the processing devices. The results demonstrate significant promise, but several areas for additional work have been identified.

Within the past decades, wavelets and associated wavelet transforms have been intensively investigated in both applied and pure mathematics. They and the related multi-scale analysis provide essential tools to describe, analyse and modify signals, images or, in rather abstract concepts, functions, function spaces and associated operators. We introduce the concept of diffusive wavelets where the dilation operator is provided by an evolution like process that comes from an approximate identity. The translation operator is naturally defined by a regular representation of the Lie group where we want to construct wavelets. For compact Lie groups the theory can be formulated in a very elegant way and also for homogeneous spaces of those groups we formulate the theory in the theory of non-commutative harmonic analysis. Explicit realisation are given for the Rotation group SO(3), the k-Torus, the Spin group and the n-sphere as homogeneous space. As non compact example we discuss diffusive wavelets on the Heisenberg group, where the construction succeeds thanks to existence of the Plancherel measure for this group. The last chapter is devoted to the Radon transform on SO(3), where the application on diffusive wavelets can be used for its inversion. The discussion of a variational spline approach provides criteria for the choice of points for measurements in concrete applications.

In this paper I formulate an explicit wavelet transform that, applied to any distribution in S^1(R^2), yields a function on phase space whose high-frequency singularities coincide precisely with the wave front set of the distribution. This characterizes the wave front set of a distribution in terms of the singularities of its wavelet transform with respect to a suitably chosen basic wavelet.

Nowadays, still images are used everywhere in the digital world. The shortages of storage capacity and transmission bandwidth make efficient compression solutions essential. A revolutionary mathematics tool, wavelet transform, has already shown its power in image processing. Minimage, the major topic of this paper, is an application that compresses still images by wavelets. Minimage is used to compress grayscale images and true color images. It implements the wavelet transform to code standard BMP image files to LET wavelet image files, which is defined in Minimage. The code is written in C++ on the Microsoft Windows NT platform. This paper illustrates the design and implementation details in MinImage according to the image compression stages. First, the preprocessor generates the wavelet transform blocks. Second, the basic wavelet decomposition is applied to transform the image data to the wavelet coefficients. The discrete wavelet transforms are the kernel component of MinImage and are discussed in detail. The different wavelet transforms can be plugged in to extend the functionality of MinImage. The third step is the quantization. The standard scalar quantization algorithm and the optimized quantization algorithm, as well as the dequantization, are described. The last part of MinImage is the entropy-coding schema. The reordering of the coefficients based on the Peano Curve and the different entropy coding methods are discussed. This paper also gives the specification of the wavelet compression parameters adjusted by the end user. The interface, parameter specification, and analysis of MinImage are shown in the final appendix.

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It is well known that in many areas of computational mathematics, wavelet based algorithms are becoming popular for modeling and analyzing data and for providing efficient means for hierarchical data decomposition of function spaces into mutually orthogonal wavelet spaces. Wavelet construction in more than one-dimensional setting is a very challenging and important research topic. In this thesis, we first introduce the method of construction wavelets by using semi-wavelets. Second, we construct piecewise linear wavelets with smaller support over type-2 triangulations. Then, parameterized wavelets are constructed using the orthogonality conditions.

The objective of the current work is to develop novel procedures for the analysis of functional data and apply them for investigation of gender disparity in survival of lung cancer patients. In particular, we use the time-dependent Cox proportional hazards model where the clinical information is incorporated via time-independent covariates, and the current age is modeled using its expansion over wavelet basis functions. We developed computer algorithms and applied them to the data set which is derived from Florida Cancer Data depository data set (all personal information which allows to identify patients was eliminated). We also studied the problem of estimation of a continuous matrix-variate function of low rank. We have constructed an estimator of such function using its basis expansion and subsequent solution of an optimization problem with the Schattennorm penalty. We derive an oracle inequality for the constructed estimator, study its properties via simulations and apply the procedure to analysis of Dynamic Contrast medical imaging data.

Various biomedical technologies like CT, MRI and PET scanners provide detailed cross-sectional views of the human anatomy. The image information obtained from these scanning devices is typically represented as large data sets whose sizes vary from several hundred megabytes to about one hundred gigabytes. As these data sets cannot be stored on one's local hard drive, SDSC provides a large data repository to store such data sets. These data sets need to be accessed by researchers around the world to collaborate in their research. But the size of these data sets make them difficult to be transmitted over the current network. This thesis presents a 3-D Haar wavelet algorithm which enables these data sets to be transformed into smaller hierarchical representations. These transformed data sets are transmitted over the network and reconstructed to a 3-D volume on the client's side through progressive refinement of the images and 3-D texture mapping techniques.