Interactive Multiscale Tensor Reconstruction for Multiresolution Volume Visualization

Suter, Susanne K.; Guitián, José Antonio Iglesias; Marton, Fabio; Agus, Marco; Elsener, A.; Zollikofer, Christoph P. E.; Gopi, M.; Gobbetti, Enrico; Pajarola, Renato

doi:10.1109/tvcg.2011.214

Cited by 46 publications

(58 citation statements)

References 21 publications

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“…linear in [33], non-linear in [21]), but simply to reduce precision storage costs and not as the primary method to eliminate coefficients. Given the proven usefulness of quantization to reduce the overall size, and in order to evaluate our method in a realistic setting, we choose to combine thresholding with a Lossy Volume Compression using Tucker Truncation and Thresholding 3 non-linear quantization step applied a posteriori.…”

Section: Related Workmentioning

confidence: 99%

“…In order to account for this effect, we incorporate in our experiments a logarithmic quantization scheme of the Tucker core, similar to [21]. Every coefficient x ∈ B gets scaled to 9 bits.…”

Section: Logarithmic Quantizationmentioning

confidence: 99%

“…As a bonus, the procedure reduces the number of basis functions (factor matrix elements) that need to be stored. While a naive Tucker strategy traversing each core element individually would take time O(I 1 I 2 I 3 R 1 R 2 R 3 ), it has been shown that the reconstruction can be efficiently performed in time O(I 1 I 2 I 3 R 1 + I 2 I 3 R 1 R 2 + I 3 R 1 R 2 R 3 ) = O(I 1 I 2 I 3 R 1 ) by exploiting the compact structure and applying successive unfoldings [21,22]. Tensor unfolding, also known as matricization, expresses an N-dimensional tensor as a matrix by slicing it and stitching together the resulting slabs.…”

Section: Reconstruction Timementioning

confidence: 99%

“…Well known examples include discrete cosine transform (DCT) based JPEG image or MPEG video compression, as well as wavelet transform (WT) based image, video and 3D volume data compression methods. In the context of compact visual data representation, tensor approximation (TA) methods have recently been shown to be a powerful domain transformation alternative [30,31,26,34,32,33,23,5,21,27,22,17].…”

Section: Introductionmentioning

confidence: 99%

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Lossy volume compression using Tucker truncation and thresholding

Ballester-Ripoll

Pajarola

2015

Vis Comput

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Tensor decompositions, in particular the Tucker model, are a powerful family of techniques for dimensionality reduction and are being increasingly used for compactly encoding large multidimensional arrays, images and other visual data sets. In interactive applications, volume data often needs to be decompressed and manipulated dynamically; when designing data reduction and reconstruction methods, several parameters must be taken into account, such as the achievable compression ratio, approximation error and reconstruction speed. Weighing these variables in an effective way is challenging, and here we present two main contributions to solve this issue for Tucker tensor decompositions. First, we provide algorithms to efficiently compute, store and retrieve good choices of tensor rank selection and decompression parameters in order to optimize memory usage, approximation quality and computational costs. Second, we propose a Tucker compression alternative based on coefficient thresholding and zigzag traversal, followed by logarithmic quantization on both the transformed tensor core and its factor matrices. In terms of approximation accuracy, this approach is theoretically and empirically better than the commonly used tensor rank truncation method. Abstract Tensor decompositions, in particular the Tucker model, are a powerful family of techniques for dimensionality reduction and are being increasingly used for compactly encoding large multidimensional arrays, images and other visual data sets. In interactive applications, volume data often needs to be decompressed and manipulated dynamically; when designing data reduction and reconstruction methods, several parameters must be taken into account, such as the achievable compression ratio, approximation error and reconstruction speed. Weighing these variables in an effective way is challenging, and here we present two main contributions to solve this issue for Tucker tensor decompositions. First, we provide algorithms to efficiently compute, store and retrieve good choices of tensor rank selection and decompression parameters in order to optimize memory usage, approximation quality and computational costs. Second, we propose a Tucker compression alternative based on coefficient thresholding and zigzag traversal, followed by logarithmic quantization on both the transformed tensor core and its factor matrices. In terms of approximation accuracy, this approach is theoretically and empirically better than the commonly used tensor rank truncation method.

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Section: Related Workmentioning

confidence: 99%

Section: Logarithmic Quantizationmentioning

confidence: 99%

Section: Reconstruction Timementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Lossy volume compression using Tucker truncation and thresholding

Ballester-Ripoll

Pajarola

2015

Vis Comput

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“…Compression-domain DVR is an ongoing active research area, nevertheless, SVD-like methods have only recently been exploited [6,28,29]. One reason is that the extension of the SVD or principal component analysis (PCA) to higher orders, beyond matrices, is not trivial.…”

Section: Compact Volume Representationmentioning

confidence: 99%

Analysis of tensor approximation for compression-domain volume visualization

Ballester-Ripoll

Suter

Pajarola

2015

Computers & Graphics

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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. Addi-tionally, 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 com-pression 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. AbstractAs 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 higherorder 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.

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Multiuser collaborative illustration and visualization for volumetric scientific data

et al. 2020

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Multiuser can collaboratively complete complex visualization tasks that cannot be completed by a single user. Although multiuser collaboration system has made great progress, there are many challenges in the collaborative visualization of 3D volumetric scientific data due to the difficulties in multiuser collaboration, and collaborative slice analysis. This article proposes a client‐server based collaborative visualization system, which consists of a 3D volume explorer and a 2D slice analyzer, to help domain experts to fully utilize their background domain knowledge to illustrate different parts of the data. For example, the brain surgeon expert, pulmonologist, and cardiologist can visualize and analyze the different subsets of the volumetric scientific data, that is, the corresponding subvolumes of the brain, heart, lungs, and blood vessels. It also allows taking full advantage of the hardware resources, because all the computation intensive tasks especially for the whole data rendering can be allocated to the powerful server while the light‐weight tasks can be allocated to the portable clients. Besides, we design a seed point tracing algorithm based on flood fill algorithm to illustrate the slice more efficiently. We evaluate the system by collecting the feedback from domain experts and the people who are unfamiliar with data computation or visualization. The evaluation shows that the 3D volume explorer and the 2D slice analyzer are capable of supporting peer‐expert discussion and medical case teaching, respectively.

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Interactive Multiscale Tensor Reconstruction for Multiresolution Volume Visualization

Cited by 46 publications

References 21 publications

Lossy volume compression using Tucker truncation and thresholding

Lossy volume compression using Tucker truncation and thresholding

Analysis of tensor approximation for compression-domain volume visualization

Multiuser collaborative illustration and visualization for volumetric scientific data

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