Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

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

Processing and codification images based on jpg standard

This project raises the necessity to use the image compression currently, and the different methods of compression and codification. Specifically, it will deepen the lossy compression standards with the JPEG [1] standard. The main goal of this project is to implement a Matlab program, which encode and compress an image of any format in a “jpg” format image, through JPEG standard premises. JPEG compresses images based on their spatial frequency, or level of detail in the image. Areas with low levels of detail, like blue sky, are compressed better than areas with high levels of detail, like hair, blades of trees, or hard-edged transitions. The JPEG algorithm takes advantage of the human eye's increased sensitivity to small differences in brightness versus small differences in color, especially at higher frequencies. The JPEG algorithm first transforms the image from RGB to the luminance/chrominance (Y-Cb-Cr) color space, or brightness/grayscale (Y) from the two color components. The algorithm then downsamples the color components and leaves the brightness component alone. Next, the JPEG algorithm approximates 8x8 blocks of pixels with a base value representing the average, plus some frequency coefficients for nearby variations. Quantization, then downsamples these DCT coefficients. Higher frequencies and chroma are quantized by larger coefficients than lower frequencies and luminance. Thus more of the brightness information is kept than the higher frequencies and color values. So the lower the level of detail and the fewer abrupt color or tonal transitions, the more efficient the JPEG algorithm becomes. ____________________________________________________________________________________________________________________________En este proyecto se aborda la necesidad de comprimir las imágenes en la actualidad, además de explicar los diferentes métodos posibles para la compresión y codificación de imágenes. En concreto, se va a profundizar en los estándares de compresión con pérdidas, mediante el estándar JPEG. El pilar central del proyecto será la realización de un programa en Matlab que codifique y comprima una imagen de cualquier formato en una imagen con formato “jpg”, mediante las premisas del estándar JPEG. La compresión de imágenes con JPEG está basada en la frecuencia espacial, o nivel de detalle, de las imágenes. Las áreas con bajo nivel de detalle, es decir, homogéneas, se pueden comprimir mejor que áreas con gran nivel de detalle o las transiciones de los bordes. El algoritmo JPEG se aprovecha de la sensibilidad del ojo humano a pequeñas diferencias de brillo frente a las de color, especialmente con altas frecuencias. El algoritmo JPEG primero transforma la paleta de colores de la imagen RGB a un espacio de color de luminancia/crominancia (Y-Cb-Cr), o brillo/escala de grises (Y) con las dos componentes del color. El algoritmo a continuación disminuye las componentes del color y deja solo la componente del brillo. A continuación, se aproxima la imagen en bloques de 8x8 pixeles con un valor base promedio, además de coeficientes de frecuencia de variaciones cercanas. Con la cuantificación, se disminuyen la resolución de los coeficientes de la DCT. Las frecuencias más altas y crominancias se cuantifican con los coeficientes de bajas frecuencias y luminancia. De esta forma, se mantienen mayor información de brillo que de altas frecuencias y colores. Por lo tanto, cuanto más homogénea sea la imagen (menor nivel de detalle y menos transiciones tonales abruptas) más eficiente será el algoritmo JPEG.Ingeniería Técnica en Sistemas de Telecomunicació

Bedarfsgesteuerte Bildübertragung mit Regions of Interest und Levels of Detail für mobile Umgebungen

Die hier entwickelten Konzepte der bedarfsgesteuerten Bildübertragung erlauben es, für wählbare Bildregionen die benötigte Detaillierungsstufe festzulegen und Bildbereiche redundanzfrei zu verfeinern. Dadurch kann die Übertragung großer Bilder an beschränkte Ressourcen in mobilen Umgebungen angepasst werden. Ein allgemeines waveletbasiertes Kodier- und Übertragungsverfahren und ein spezielles progressives Kodierverfahren für Farbtabellenbilder werden entworfen. Zur effizienten Ausnutzung von Bildschirmfläche und Bitrate wird die interaktive Technik des Rechteckigen Fisheye-View entwickelt.The proposed concept of demand-driven image transmission allows to define the required level of detail for arbitrary image regions and to refine image areas without the need for redundant data transmissions. By this mechanism the image transmission can be better adapted to the limited resources in mobile environments. A general wavelet-based method for image encoding and transmission and a specific encoding method for palettized images are designed. For the efficient use of screen area and transmission bit rate the interactive technique of the Rectangular Fish Eye View is developed

Space-optimized texture atlases

Texture atlas parameterization provides an effective way to map a variety of colour and data attributes from 2D texture domains onto polygonal surface meshes. Most of the existing literature focus on how to build seamless texture atlases for continuous photometric detail, but little e ort has been devoted to devise e cient techniques for encoding self-repeating, uncontinuous signals such as building facades. We present a perception-based scheme for generating space-optimized texture atlases speci cally designed for intentionally non-bijective parameterizations. Our scheme combines within-chart tiling support with intelligent packing and perceptual measures for assigning texture space in accordance to the amount of information contents of the image and on its saliency. We demonstrate our optimization scheme in the context of real-time navigation through a gigatexel urban model of an European city. Our scheme achieves signi cant compression ratios and speed-up factors with visually indistinguishable results. We developed a technique that generates space-optimized texture atlases for the particular encoding of uncontinuous signals projected onto geometry. The scene is partitioned using a texture atlas tree that contains for each node a texture atlas. The leaf nodes of the tree contain scene geometry. The level of detail is controlled by traversing the tree and selecting the appropriate texture atlas for a given viewer position and orientation. In a preprocessing step, textures associated to each texture atlas node of the tree are packed. Textures are resized according to a given user-de ned texel size and the size of the geometry that are projected onto. We also use perceptual measures to assign texture space in accordance to image detail. We also explore different techniques for supporting texture wrapping of uncontinuous signals, which involved the development of e cient techniques for compressing texture coordinates via the GPU. Our approach supports texture ltering and DXTC compression without noticeable artifacts. We have implemented a prototype version of our space-optimized texture atlases technique and used it to render the 3D city model of Barcelona achieving interactive rendering frame rates. The whole model was composed by more than three million triangles and contained more than twenty thousand different textures representing the building facades with an average original resolution of 512 pixels per texture. Our scheme achieves up 100:1 compression ratios and speed-up factors of 20 with visually indistinguishable results