Susanne K. Suter

State-of-the-Art in Compressed GPU-Based Direct Volume Rendering

Great advancements in commodity graphics hardware have favoured graphics processing unit (GPU)‐based volume rendering as the main adopted solution for interactive exploration of rectilinear scalar volumes on commodity platforms. Nevertheless, long data transfer times and GPU memory size limitations are often the main limiting factors, especially for massive, time‐varying or multi‐volume visualization, as well as for networked visualization on the emerging mobile devices. To address this issue, a variety of level‐of‐detail (LOD) data representations and compression techniques have been introduced. In order to improve capabilities and performance over the entire storage, distribution and rendering pipeline, the encoding/decoding process is typically highly asymmetric, and systems should ideally compress at data production time and decompress on demand at rendering time. Compression and LOD pre‐computation does not have to adhere to real‐time constraints and can be performed off‐line for high‐quality results. In contrast, adaptive real‐time rendering from compressed representations requires fast, transient and spatially independent decompression. In this report, we review the existing compressed GPU volume rendering approaches, covering sampling grid layouts, compact representation models, compression techniques, GPU rendering architectures and fast decoding techniques.

Harris lines revisited: Prevalence, comorbidities, and possible etiologies

Objectives: The occurrence of transverse radiopaque lines in long bones—Harris lines (HLs)—is correlated with episodes of temporary arrest of longitudinal growth and has been used as an indicator of health and nutritional status of modern and historical populations. However, the interpretation of HLs as a stress indicator remains debatable. The aim of this article is to evaluate the perspectives and the limitations of HLs analyses and to examine their reliability as a stress indicator.

Interactive Multiscale Tensor Reconstruction for Multiresolution Volume Visualization

Large scale and structurally complex volume datasets from high-resolution 3D imaging devices or computational simulations pose a number of technical challenges for interactive visual analysis. In this paper, we present the first integration of a multiscale volume representation based on tensor approximation within a GPU-accelerated out-of-core multiresolution rendering framework. Specific contributions include (a) a hierarchical brick-tensor decomposition approach for pre-processing large volume data, (b) a GPU accelerated tensor reconstruction implementation exploiting CUDA capabilities, and (c) an effective tensor-specific quantization strategy for reducing data transfer bandwidth and out-of-core memory footprint. Our multiscale representation allows for the extraction, analysis and display of structural features at variable spatial scales, while adaptive level-of-detail rendering methods make it possible to interactively explore large datasets within a constrained memory footprint. The quality and performance of our prototype system is evaluated on large structurally complex datasets, including gigabyte-sized micro-tomographic volumes.

TAMRESH - tensor approximation multiresolution hierarchy for interactive volume visualization

Interactive visual analysis of large and complex volume datasets is an ongoing and challenging problem. We tackle this challenge in the context of state‐of‐the‐art out‐of‐core multiresolution volume rendering by introducing a novel hierarchical tensor approximation (TA) volume visualization approach. The TA framework allows us (a) to use a rank‐truncated basis for compact volume representation, (b) to visualize features at multiple scales, and (c) to visualize the data at multiple resolutions. In this paper, we exploit the special properties of the TA factor matrix bases and define a novel multiscale and multiresolution volume rendering hierarchy. Different from previous approaches, to represent one volume dataset we use but one set of global bases (TA factor matrices) to reconstruct at all resolution levels and feature scales. In particular, we propose a coupling of multiscalable feature visualization and multiresolution DVR through the properties of global TA bases. We demonstrate our novel TA multiresolution hierarchy based volume representation and visualization on a number of μCT volume datasets.

A Survey of Compressed GPU-Based Direct Volume Rendering

Great advancements in commodity graphics hardware have favored GPU-based volume rendering as the main adopted solution for interactive exploration of rectilinear scalar volumes on commodity platforms. Nevertheless, long data transfer times and GPU memory size limitations are often the main limiting factors, especially for massive, time-varying or multi-volume visualization, or for networked visualization on the emerging mobile devices. To address this issue, a variety of level-of-detail data representations and compression techniques have been introduced. In order to improve capabilities and performance over the entire storage, distribution and rendering pipeline, the encoding/decoding process is typically highly asymmetric, and systems should ideally compress at data production time and decompress on demand at rendering time. Compression and level-of-detail pre-computation does not have to adhere to real-time constraints and can be performed off-line for high quality results. In contrast, adaptive real-time rendering from compressed representations requires fast, transient, and spatially independent decompression. In this report, we review the existing compressed GPU volume rendering approaches, covering compact representation models, compression techniques, GPU rendering architectures and fast decoding techniques.

On best rank one approximation of tensors

Today, compact and reduced data representations using low rank data approximation are common to represent high-dimensional data sets in many application areas as for example genomics, multimedia, quantum chemistry, social networks or visualization. In order to produce such low rank data representations, the input data is typically approximated by so-called alternating least squares (ALS) algorithms. However, not all of these ALS algorithms are guaranteed to converge. To address this issue, we suggest a new algorithm for the computation of a best rank one approximation of tensors, called alternating singular value decomposition. This method is based on the computation of maximal singular values and the corresponding singular vectors of matrices. We also introduce a modification for this method and the alternating least squares method, which ensures that alternating iterations will always converge to a semi-maximal point. (A critical point in several vector variables is semi-maximal if it is maximal with respect to each vector variable, while other vector variables are kept fixed.) We present several numerical examples that illustrate the computational performance of the new method in comparison to the alternating least square method.

Application of Tensor Approximation to Multiscale Volume Feature Representations

Advanced 3D microstructural analysis in natural sciences and engineering depends ever more on modern data acquisition and imaging technologies such as micro-computed or synchrotron tomography and interactive visualization. The acquired volume data sets are not only of high-resolution but in particular exhibit complex spatial structures at different levels of scale (e.g. variable spatial expression of multiscale periodic growth structures in tooth enamel). Such highly structured volume data sets represent a tough challenge to be analyzed and explored by means of interactive visualization due to the amount of raw volume data to be processed and filtered for the desired features. As an approach to address this bottleneck by multiscale feature preserving data reduction, we propose higher-order tensor approximations (TAs). We demonstrate the power of TA to represent, and highlight the structural features in volume data. We visually and quantitatively show that TA yields high data reduction and that TA preserves volume features at multiple scales.

Analysis of tensor approximation for compression-domain volume visualization

As modern high-resolution imaging devices allow to acquire increasingly large and complex volume data sets, their effective and compact representation for visualization becomes a challenging task. The Tucker decomposition has already confirmed higher-order tensor approximation (TA) as a viable technique for compressed volume representation; however, alternative decomposition approaches exist. In this work, we review the main TA models proposed in the literature on multiway data analysis and study their application in a visualization context, where reconstruction performance is emphasized along with reduced data representation costs. Progressive and selective detail reconstruction is a main goal for such representations and can efficiently be achieved by truncating an existing decomposition. To this end, we explore alternative incremental variations of the CANDECOMP/PARAFAC and Tucker models. We give theoretical time and space complexity estimates for every discussed approach and variant. Additionally, their empirical decomposition and reconstruction times and approximation quality are tested in both C++ and MATLAB implementations. Several scanned real-life exemplar volumes are used varying data sizes, initialization methods, degree of compression and truncation. As a result of this, we demonstrate the superiority of the Tucker model for most visualization purposes, while canonical-based models offer benefits only in limited situations. Graphical abstractDisplay Omitted HighlightsWe explore tensor decomposition techniques in the field of volume visualization.We contribute and compare alternative incremental variants.We provide time and space complexity estimates for these approaches and variants.We demonstrate the superiority of the Tucker model in most 3D visualization purposes.

Visual Analysis of 3D Data by Isovalue Clustering

Visualization of volumetric data is ubiquitous in data analysis and has been widely used for exploration in scientific simulations and biomedical imaging. While direct and indirect visualization algorithms are employed extensively in applications, the visual exploration of features in the volumetric data is still a laborious task. We present an algorithm to extract exemplar isosurfaces from a 3D scalar field data set and provide the user with a representative visualization of the data. The presented approach provides an interactive tool that aids in visual analysis and exploration tasks. Our experiments on a number of benchmark data sets suggest that, compared to existing methods, the proposed approach provides a more distinct set of isosurfaces that are more representative of the complexity of the data sets.

Quality assessment of volume compression approaches using isovalue clustering

We provide an interactive tool for extracting exemplar isosurfaces from a 3D scalareld using a novel isovalue classication process.We propose a structural VQA metric that uses representative isosurfaces as benchmark structures to assess the visual quality of compressed 3D scalarelds.Our experiments on a number of benchmark data sets suggest that, compared to existing methods, the proposed isovalue classication approach provides a more distinct set of isosurfaces that are more representative of the complexity of the datasets.We examine a number of widely-used compression techniques (i.e.,discrete wavelet transform, discrete cosine transform, and tensor approximation) to establish the utility of our VQA approach. Display Omitted Visualization of volumetric data has been widely used for exploration of data from scientific simulations and biomedical imaging. Despite advances of GPU-assisted rendering, which has become the state-of-art in direct volume rendering, still many volumetric data sets are too large to be visualized interactively. Therefore, compression-domain rendering approaches are used in visualization processes in order to reduce the amount of data sent to the GPU and thus speed up interactive visualization. Hence, reliable tools to assess the quality of the reconstructed 3D data are of great importance, influencing the effectiveness of the visualization. However, numerical error analysis approaches such as mean-squared-based metrics are often inconsistent with perceived visual quality. We propose a structural volume quality assessment approach for 3D scalar volume based on the human visual system (HVS). Our approach consists of two stages: First, we provide an interactive tool for extracting significant volume features via isosurfaces from a 3D scalar field using an isovalue classification process. Second, we propose a structural volume quality assessment (VQA) metric that employs representative isosurfaces as benchmark structures. For this purpose, we use a recently developed perceptual-based mesh quality metric [1] to assess the visual quality of compressed 3D scalar fields. Our experiments on a number of benchmark data sets suggest that, compared to existing methods, the proposed isovalue classification approach provides a more distinct set of isosurfaces that are more representative of the complexity of the data sets. We examine a number of widely used compression approaches, namely, discrete wavelet transform, discrete cosine transform, and tensor approximation, to establish the utility of our volume quality assessment approach.