Topographic map visualization from adaptively compressed textures

Raster-based topographic maps are commonly used in geoinformation systems to overlay geographic entities on top of digital terrain models. Using compressed texture formats for encoding topographic maps allows reducing latency times while visualizing large geographic datasets. Topographic maps encompass high-frequency content with large uniform regions, making current compressed texture formats inappropriate for encoding them. In this paper we present a method for locally-adaptive compression of topographic maps. Key elements include a Hilbert scan to maximize spatial coherence, efficient encoding of homogeneous image regions through arbitrarily-sized texel runs, a cumulative run-length encoding supporting fast random-access, and a compression algorithm supporting lossless and lossy compression. Our scheme can be easily implemented on current programmable graphics hardware allowing real-time GPU decompression and rendering of bilinear-filtered topographic maps.Postprint (published version

Fast Compressed Segmentation Volumes for Scientific Visualization

Voxel-based segmentation volumes often store a large number of labels and voxels, and the resulting amount of data can make storage, transfer, and interactive visualization difficult. We present a lossless compression technique which addresses these challenges. It processes individual small bricks of a segmentation volume and compactly encodes the labelled regions and their boundaries by an iterative refinement scheme. The result for each brick is a list of labels, and a sequence of operations to reconstruct the brick which is further compressed using rANS-entropy coding. As the relative frequencies of operations are very similar across bricks, the entropy coding can use global frequency tables for an entire data set which enables efficient and effective parallel (de)compression. Our technique achieves high throughput (up to gigabytes per second both for compression and decompression) and strong compression ratios of about 1% to 3% of the original data set size while being applicable to GPU-based rendering. We evaluate our method for various data sets from different fields and demonstrate GPU-based volume visualization with on-the-fly decompression, level-of-detail rendering (with optional on-demand streaming of detail coefficients to the GPU), and a caching strategy for decompressed bricks for further performance improvement.Comment: IEEE Vis 202

Lossless Compression for Semantic Textures

A semantic texture overlays semantic labels over an image to indicate the type of texture represented by each region of the image. Traditional lossy compression works well for color textures, but not for semantic textures. This disclosure describes lossless compression techniques to compress semantic textures, thereby reducing the memory occupied by semantic textures. The techniques leverage the observation that semantic textures, unlike color textures, are highly structured with large blocks of common values. The techniques enable high-speed access to detailed rendering and resolution in real-time computer graphics. They also enable additional textures or texture resolution, enhancing the detail and realism of rendering

Photo Based 3D Walkthrough

The objective of 'Photo Based 3D Walkthrough' is to understand how image-based rendering technology is used to create virtual environment and to develop aprototype system which is capable ofproviding real-time 3D walkthrough experience by solely using 2D images. Photo realism has always been an aim of computer graphics in virtual environment. Traditional graphics needs a great amount of works and time to construct a detailed 3D model andscene. Despite the tedious works in constructing the 3D models andscenes, a lot ofefforts need to beput in to render the constructed 3D models and scenes to enhance the level of realism. Traditional geometry-based rendering systems fall short ofsimulating the visual realism of a complex environment and are unable to capture and store a sampled representation ofa large environment with complex lighting and visibility effects. Thus, creating a virtual walkthrough ofa complex real-world environment remains one of the most challenging problems in computer graphics. Due to the various disadvantages of the traditional graphics and geometry-based rendering systems, image-based rendering (IBR) has been introduced recently to overcome the above problems. In this project, a research will be carried out to create anIBR virtual walkthrough by using only OpenGL and C++program without the use of any game engine or QuickTime VR function. Normal photographs (not panoramic photographs) are used as the source material in creating the virtual scene and keyboard is used asthe main navigation tool in the virtual environment. The quality ofthe virtual walkthrough prototype constructed isgood withjust a littlejerkiness

Exploiting spatial and temporal coherence in GPU-based volume rendering

Effizienz spielt eine wichtige Rolle bei der Darstellung von Volumendaten, selbst wenn leistungsstarke Grafikhardware zur Verfügung steht, da steigende Datensatzgrößen und höhere Anforderungen an Visualisierungstechniken Fortschritte bei Grafikprozessoren ausgleichen. In dieser Dissertation wird untersucht, wie räumliche und zeitliche Kohärenz in Volumendaten zur Optimierung von Volumenrendering genutzt werden kann. Es werden mehrere neue Ansätze für statische und zeitvariante Daten eingeführt, die verschieden Arten von Kohärenz in verschiedenen Stufen der Volumenrendering-Pipeline ausnutzen. Zu den vorgestellten Beschleunigungstechniken gehört Empty Space Skipping mittels Occlusion Frustums, eine auf Slabs basierende Cachestruktur für Raycasting und ein verlustfreies Kompressionsscheme für zeitvariante Daten. Die Algorithmen wurden zur Verwendung mit GPU-basiertem Volumen-Raycasting entworfen und nutzen die Fähigkeiten moderner Grafikprozessoren, insbesondere Stream Processing. Efficiency is a key aspect in volume rendering, even if powerful graphics hardware is employed, since increasing data set sizes and growing demands on visualization techniques outweigh improvements in graphics processor performance. This dissertation examines how spatial and temporal coherence in volume data can be used to optimize volume rendering. Several new approaches for static as well as for time-varying data sets are introduced, which exploit different types of coherence in different stages of the volume rendering pipeline. The presented acceleration algorithms include empty space skipping using occlusion frustums, a slab-based cache structure for raycasting, and a lossless compression scheme for time-varying data. The algorithms were designed for use with GPU-based volume raycasting and to efficiently exploit the features of modern graphics processors, especially stream processing

Techniques for Large Data Visualization

pages 315-32

Compressed Random-Access Trees for Spatially Coherent Data

International audienceAdaptive multiresolution hierarchies are highly efficient at representing spatially coherent graphics data. We introduce a framework for compressing such adaptive hierarchies using a compact randomly-accessible tree structure. Prior schemes have explored compressed trees, but nearly all involve entropy coding of a sequential traversal, thus preventing fine-grain random queries required by rendering algorithms. Instead, we use fixed-rate encoding for both the tree topology and its data. Key elements include the replacement of pointers by local offsets, a forested mipmap structure, vector quantization of inter-level residuals, and efficient coding of partially defined data. Both the offsets and codebook indices are stored as byte records for easy parsing by either CPU or GPU shaders. We show that continuous mipmapping over an adaptive tree is more efficient using primal subdivision than traditional dual subdivision. Finally, we demonstrate efficient compression of many data types including light maps, alpha mattes, distance fields, and HDR images

Incremental volume rendering using hierarchical compression

Includes bibliographical references.The research has been based on the thesis that efficient volume rendering of datasets, contained on the Internet, can be achieved on average personal workstations. We present a new algorithm here for efficient incremental rendering of volumetric datasets. The primary goal of this algorithm is to give average workstations the ability to efficiently render volume data received over relatively low bandwidth network links in such a way that rapid user feedback is maintained. Common limitations of workstation rendering of volume data include: large memory overheads, the requirement of expensive rendering hardware, and high speed processing ability. The rendering algorithm presented here overcomes these problems by making use of the efficient Shear-Warp Factorisation method which does not require specialised graphics hardware. However the original Shear-Warp algorithm suffers from a high memory overhead and does not provide for incremental rendering which is required should rapid user feedback be maintained. Our algorithm represents the volumetric data using a hierarchical data structure which provides for the incremental classification and rendering of volume data. This exploits the multiscale nature of the octree data structure. The algorithm reduces the memory footprint of the original Shear-Warp Factorisation algorithm by a factor of more than two, while maintaining good rendering performance. These factors make our octree algorithm more suitable for implementation on average desktop workstations for the purposes of interactive exploration of volume models over a network. This dissertation covers the theory and practice of developing the octree based Shear-Warp algorithms, and then presents the results of extensive empirical testing. The results, using typical volume datasets, demonstrate the ability of the algorithm to achieve high rendering rates for both incremental rendering and standard rendering while reducing the runtime memory requirements