In order to achieve more realistic visual effect, the texturing mapping has become a very important and popular technique used in three-dimensional (3D) graphic. Many advanced rendering effects including shadow, environment, and bump mapping all depend on various applications of texturing function. Therefore, how to design an efficient texture unit is very important for 3D graphic rendering system. This thesis proposes an advanced texture unit design targeted for the rendering system with the fill rate of two fragments per cycle. This unit can support various filtering functions including nearest neighbor, bi-linear and tri-linear filtering. It can also provide the mip-map function to automatically select the best texture images for rendering. In order to realize the high texel throughput requirement for some complex filtering function, the texture cache has been divided into four banks such that up to eight texels can be delivered every cycle. The data-path design for the filtering unit has adopted the common expression sharing technique to reduce the required arithmetic units. The proposed texturing unit architecture has been implemented and embedded into a 3D rendering accelerator which has been integrated with OpenGL-ES software module, Linux operation system and geometry module, and successfully prototyped on the ARM versatile platform. With the 0.18um technology, this unit can run up to 150 Mhz, and provide the peak throughput of 1.2G texel/s.

With the growing demand of embedded graphics applications, how to provide an efficient graphics hardware acceleration solution has drawn much attention. It is well known that computer graphics contains two major domains: two-dimensional (2D) and three-dimensional (3D) graphics. Each domain owns large amounts of applications, such that general embedded platforms will require both graphics acceleration supports. This thesis proposes an advanced texture unit architecture which can provide various 3D texture filtering functions including trilinear, anistrophics filtering etc , and 2D coloring, painting, and texturing functions. Our proposed design consists of a core computation unit, and a set of data registers. The equations for those supported functions are decomposed into a series of basic arithmetic operations such as multiply-add-accumulation, multiply, etc executed by the core computation unit. To evaluate those equations for each pixel may require some pre-computed parameters which will be computed outside our unit in advance by the system¡¦s micro-controller. The equations can be computed by our texture unit based on the selected finite-state machine sequences which is stored in the on-chip control table. By updating those sequences can change the functionality provided by our chip. The overall cost of the proposed unit is about 28.36k gates. In addition to various texturing functions, this thesis also proposes an implementation of texture function for high-dynamic range (HDR) textures. HDR textures can provide various color details according to the frame¡¦s global illumination environment. Therefore, the 3D rendering system has to incorporate a tone-mapping mechanism to map the HDR image into normal color range of output display system. To reduce the overall tone-mapping implementation cost, this thesis uses an extra accumulator between the standard per-fragment rendering pipeline stages to accumulate the global illumination intensity based on the depth comparison result of the incoming pixel. After all of the pixels have passed through the pipeline stages, every pixel of the stored rendering result will be fetched into a mapping unit which will generate its mapping color in the normal dynamic range. The overall cost of the additional hardware for the realization of HDR textures is about 6.98k gates.

Résumé : En télédétection, la texture facilite l’identification des classes de surfaces sur des critères de similitude d’organisation spatiale des pixels. Les méthodes d’analyse texturale utilisées en télédétection et en traitement d’image en général sont principalement proposées pour extraire la texture dans une seule bande à la fois. Pour les images multispectrales, ceci revient à extraire la texture dans chaque bande spectrale séparément. Cette stratégie ignore la dépendance qui existe entre la texture des différentes bandes (texture inter-bande) qui peut être une source d’information additionnelle aux côtés de l’information texturale classique intra-bande. La prise en charge de la texture multibande (intra- et inter-bande) engendre une complexité calculatoire importante. Dans sa recherche de solution pour l’analyse de la texture multibande, ce projet de thèse revient vers les aspects fondamentaux de l’analyse de la texture, afin de proposer un modèle de texture qui possède intrinsèquement une complexité calculatoire réduite, et cela indépendamment de l’aspect multibande de la texture. Une solution pour la texture multibande est ensuite greffée sur ce nouveau modèle, de manière à lui permettre d’hériter de sa complexité calculatoire réduite. La première partie de ce projet de recherche introduit donc un nouveau modèle l’analyse de texture appelé modèle d’unité texturale compacte (en anglais : Compact Texture Unit, C-TU). Le C-TU prend comme point de départ le modèle de spectre de texture et propose une réduction significative de sa complexité. Cette réduction est atteinte en proposant une solution générale pour une codification de la texture avec la seule information d’occurrence, sans l’information structurelle. En prenant avantage de la grande efficacité calculatoire du modèle de C-TU développé, un nouvel indice qui analyse la texture multibande comme un ensemble indissociable d’interactions spatiales intra- et inter-bandes est proposé. Cet indice, dit C-TU multibande, utilise la notion de voisinage multibande afin de comparer le pixel central avec ses voisins dans la même bande et avec ceux des autres bandes spectrales. Ceci permet à l’indice de C-TU multibande d’extraire la texture de plusieurs bandes simultanément. Finalement, une nouvelle base de données de textures couleurs multibandes est proposée, pour une validation des méthodes texturales multibandes. Une série de tests visant principalement à évaluer la qualité discriminante des solutions proposées a été conduite. L’ensemble des résultats obtenus dont nous faisons rapport ici confirme que le modèle de C-TU proposé ainsi que sa version multibande sont des outils performants pour l’analyse de la texture en télédétection et en traitement d’images en général. Les tests ont également démontré que la nouvelle base de données de textures multibande possède toutes les caractéristiques nécessaires pour être utilisée en validation des méthodes de texture multibande. // Abstract : In multispectral images, texture is typically extracted independently in each band using existing grayscale texture methods. However, reducing texture of multispectral images into a set of independent grayscale texture ignores inter-band spatial interactions which can be a valuable source of information. The main obstacle for characterizing texture as intra- and inter-band spatial interactions is that the required calculations are cumbersome. In the first part of this PhD thesis, a new texture model named the Compact Texture Unit (C-TU) model was proposed. The C-TU model is a general solution for the texture spectrum model, in order to decrease its computational complexity. This simplification comes from the fact that the C-TU model characterizes texture using only statistical information, while the texture spectrum model uses both statistical and structural information. The proposed model was evaluated using a new monoband C-TU descriptor in the context of texture classification and image retrieval. Results showed that the monoband C-TU descriptor that uses the proposed C-TU model provides performances equivalent to those delivered by the texture spectrum model but with much more lower complexity. The calculation efficiency of the proposed C-TU model is exploited in the second part of this thesis in order to propose a new descriptor for multiband texture characterization. This descriptor, named multiband C-TU, extracts texture as a set of intra- and inter-band spatial interactions simultaneously. The multiband C-TU descriptor is very simple to extract and computationally efficient. The proposed descriptor was compared with three strategies commonly adopted in remote sensing. The first is extracting texture using panchromatic data; the second is extracting texture separately from few newbands obtained by principal components transform; and the third is extracting texture separately in each spectral band. These strategies were applied using cooccurrence matrix and monoband compact texture descriptors. For all experiments, the proposed descriptor provided the best results. In the last part of this thesis, a new color texture images database is developed, named Multiband Brodatz Texture database. Images from this database have two important characteristics. First, their chromatic content, even if it is rich, does not have discriminative value, yet it contributes to form texture. Second, their textural content is characterized by high intra- and inter-band variation. These two characteristics make this database ideal for multiband texture analysis without the influence of color information.