Interactive Level-of-Detail Selection Using Image-Based Quality Metric for Large Volume Visualization

Wang, C.; García, Ana E. Delgado; Shen, Han‐Wei

doi:10.1109/tvcg.2007.15

Cited by 40 publications

(16 citation statements)

References 30 publications

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“…For details, interested readers can refer to [13]. Wang et al [14] proposed an image-based quality metric which measures the contribution of the multiresolution data blocks to the resulting image for their multiresolution level-of-detail selection and rendering framework. They further developed a reduced-reference method for volumetric data quality measurement [15] .…”

Section: Quality Metricsmentioning

confidence: 99%

Visibility-Aware Direct Volume Rendering

Mak

Chan

et al. 2011

J. Comput. Sci. Technol.

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Direct volume rendering (DVR) is a powerful visualization technique which allows users to effectively explore and study volumetric datasets. Different transparency settings can be flexibly assigned to different structures such that some valuable information can be revealed in direct volume rendered images (DVRIs). However, end-users often feel that some risks are always associated with DVR because they do not know whether any important information is missing from the transparent regions of DVRIs. In this paper, we investigate how to semi-automatically generate a set of DVRIs and also an animation which can reveal information missed in the original DVRIs and meanwhile satisfy some image quality criteria such as coherence. A complete framework is developed to tackle various problems related to the generation and quality evaluation of visibility-aware DVRIs and animations. Our technique can reduce the risk of using direct volume rendering and thus boost the confidence of users in volume rendering systems.

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Section: Quality Metricsmentioning

confidence: 99%

Visibility-Aware Direct Volume Rendering

Mak

Chan

et al. 2011

J. Comput. Sci. Technol.

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“…There are several research efforts in volume visualization that incorporate transfer functions into multiresolution data compression and rendering [10], [20], [30], [37]. Their common goal was to adapt data precision and resolution according to the visualization content so that a better tradeoff between data compression and rendering quality can be achieved.…”

Section: Volume Data Reductionmentioning

confidence: 99%

Feature-Preserving Volume Data Reduction and Focus+Context Visualization

Wang

Lee

et al. 2011

IEEE Trans. Visual. Comput. Graphics

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Abstract-The growing sizes of volumetric data sets pose a great challenge for interactive visualization. In this paper, we present a feature-preserving data reduction and focus+context visualization method based on transfer function driven, continuous voxel repositioning and resampling techniques. Rendering reduced data can enhance interactivity. Focus+context visualization can show details of selected features in context on display devices with limited resolution. Our method utilizes the input transfer function to assign importance values to regularly partitioned regions of the volume data. According to user interaction, it can then magnify regions corresponding to the features of interest while compressing the rest by deforming the 3D mesh. The level of data reduction achieved is significant enough to improve overall efficiency. By using continuous deformation, our method avoids the need to smooth the transition between low and high-resolution regions as often required by multiresolution methods. Furthermore, it is particularly attractive for focus+context visualization of multiple features. We demonstrate the effectiveness and efficiency of our method with several volume data sets from medical applications and scientific simulations.

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“…Depending on our need, µ, σ, and σij in Eqn. 3 and 5 can be evaluated directly in the scalar data space, or in the perceptually-adapted CIELUV color space (see [23] for more detail). In Section 7.2, we will describe our pre-computation and real-time update techniques for calculating D and C of multiresolution data blocks, which ensure a quick update of the entropy value for a LOD.…”

Section: Distortion and Contributionmentioning

confidence: 99%

“…For instance, it only takes around 0.2 second to update the visibility of thousands of data blocks for the RMI data set. We refer readers to [23] for the algorithm, implementation, and performance of our summary table scheme and GPU-based visibility estimation.…”

Section: Efficiencymentioning

confidence: 99%

LOD Map - A Visual Interface for Navigating Multiresolution Volume Visualization

Wang

Shen

2006

IEEE Trans. Visual. Comput. Graphics

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Abstract-In multiresolution volume visualization, a visual representation of level-of-detail (LOD) quality is important for us to examine, compare, and validate different LOD selection algorithms. While traditional methods rely on ultimate images for quality measurement, we introduce the LOD map -an alternative representation of LOD quality and a visual interface for navigating multiresolution data exploration. Our measure for LOD quality is based on the formulation of entropy from information theory. The measure takes into account the distortion and contribution of multiresolution data blocks. A LOD map is generated through the mapping of key LOD ingredients to a treemap representation. The ordered treemap layout is used for relative stable update of the LOD map when the view or LOD changes. This visual interface not only indicates the quality of LODs in an intuitive way, but also provides immediate suggestions for possible LOD improvement through visually-striking features. It also allows us to compare different views and perform rendering budget control. A set of interactive techniques is proposed to make the LOD adjustment a simple and easy task. We demonstrate the effectiveness and efficiency of our approach on large scientific and medical data sets.

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Interactive Level-of-Detail Selection Using Image-Based Quality Metric for Large Volume Visualization

Cited by 40 publications

References 30 publications

Visibility-Aware Direct Volume Rendering

Visibility-Aware Direct Volume Rendering

Feature-Preserving Volume Data Reduction and Focus+Context Visualization

LOD Map - A Visual Interface for Navigating Multiresolution Volume Visualization

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