A statistical approach to volume data quality assessment

Chaoli Wang, Kwan-Liu Ma

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Quality assessment plays a crucial role in data analysis. In this paper, we present a reduced-reference approach to volume data quality assessment. Our algorithm extracts important statistical information from the original data in the wavelet domain. Using the extracted information as feature and predefined distance functions, we are able to identify and quantify the quality loss in the reduced or distorted version of data, eliminating the need to access the original data. Our feature representation is naturally organized in the form of multiple scales, which facilitates quality evaluation of data with different resolutions. The feature can be effectively compressed in size. We have experimented with our algorithm on scientific and medical data sets of various sizes and characteristics. Our results show that the size of the feature does not increase in proportion to the size of original data. This ensures the scalability of our algorithm and makes it very applicable for quality assessment of large-scale data sets. Additionally, the feature could be used to repair the reduced or distorted data for quality improvement. Finally, our approach can be treated as a new way to evaluate the uncertainty introduced by different versions of data.

Original languageEnglish (US)
Article number4407698
Pages (from-to)590-602
Number of pages13
JournalIEEE Transactions on Visualization and Computer Graphics
Volume14
Issue number3
DOIs
StatePublished - May 1 2008

Fingerprint

Scalability
Repair
Uncertainty

Keywords

  • Generalized Gaussian density
  • Quality assessment
  • Reduced reference
  • Statistical modeling
  • Volume visualization
  • Wavelet transform

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

Cite this

A statistical approach to volume data quality assessment. / Wang, Chaoli; Ma, Kwan-Liu.

In: IEEE Transactions on Visualization and Computer Graphics, Vol. 14, No. 3, 4407698, 01.05.2008, p. 590-602.

Research output: Contribution to journalArticle

@article{cb08c8de051047548f312544073fe2e5,
title = "A statistical approach to volume data quality assessment",
abstract = "Quality assessment plays a crucial role in data analysis. In this paper, we present a reduced-reference approach to volume data quality assessment. Our algorithm extracts important statistical information from the original data in the wavelet domain. Using the extracted information as feature and predefined distance functions, we are able to identify and quantify the quality loss in the reduced or distorted version of data, eliminating the need to access the original data. Our feature representation is naturally organized in the form of multiple scales, which facilitates quality evaluation of data with different resolutions. The feature can be effectively compressed in size. We have experimented with our algorithm on scientific and medical data sets of various sizes and characteristics. Our results show that the size of the feature does not increase in proportion to the size of original data. This ensures the scalability of our algorithm and makes it very applicable for quality assessment of large-scale data sets. Additionally, the feature could be used to repair the reduced or distorted data for quality improvement. Finally, our approach can be treated as a new way to evaluate the uncertainty introduced by different versions of data.",
keywords = "Generalized Gaussian density, Quality assessment, Reduced reference, Statistical modeling, Volume visualization, Wavelet transform",
author = "Chaoli Wang and Kwan-Liu Ma",
year = "2008",
month = "5",
day = "1",
doi = "10.1109/TVCG.2007.70628",
language = "English (US)",
volume = "14",
pages = "590--602",
journal = "IEEE Transactions on Visualization and Computer Graphics",
issn = "1077-2626",
publisher = "IEEE Computer Society",
number = "3",

}

TY - JOUR

T1 - A statistical approach to volume data quality assessment

AU - Wang, Chaoli

AU - Ma, Kwan-Liu

PY - 2008/5/1

Y1 - 2008/5/1

N2 - Quality assessment plays a crucial role in data analysis. In this paper, we present a reduced-reference approach to volume data quality assessment. Our algorithm extracts important statistical information from the original data in the wavelet domain. Using the extracted information as feature and predefined distance functions, we are able to identify and quantify the quality loss in the reduced or distorted version of data, eliminating the need to access the original data. Our feature representation is naturally organized in the form of multiple scales, which facilitates quality evaluation of data with different resolutions. The feature can be effectively compressed in size. We have experimented with our algorithm on scientific and medical data sets of various sizes and characteristics. Our results show that the size of the feature does not increase in proportion to the size of original data. This ensures the scalability of our algorithm and makes it very applicable for quality assessment of large-scale data sets. Additionally, the feature could be used to repair the reduced or distorted data for quality improvement. Finally, our approach can be treated as a new way to evaluate the uncertainty introduced by different versions of data.

AB - Quality assessment plays a crucial role in data analysis. In this paper, we present a reduced-reference approach to volume data quality assessment. Our algorithm extracts important statistical information from the original data in the wavelet domain. Using the extracted information as feature and predefined distance functions, we are able to identify and quantify the quality loss in the reduced or distorted version of data, eliminating the need to access the original data. Our feature representation is naturally organized in the form of multiple scales, which facilitates quality evaluation of data with different resolutions. The feature can be effectively compressed in size. We have experimented with our algorithm on scientific and medical data sets of various sizes and characteristics. Our results show that the size of the feature does not increase in proportion to the size of original data. This ensures the scalability of our algorithm and makes it very applicable for quality assessment of large-scale data sets. Additionally, the feature could be used to repair the reduced or distorted data for quality improvement. Finally, our approach can be treated as a new way to evaluate the uncertainty introduced by different versions of data.

KW - Generalized Gaussian density

KW - Quality assessment

KW - Reduced reference

KW - Statistical modeling

KW - Volume visualization

KW - Wavelet transform

UR - http://www.scopus.com/inward/record.url?scp=41549136621&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=41549136621&partnerID=8YFLogxK

U2 - 10.1109/TVCG.2007.70628

DO - 10.1109/TVCG.2007.70628

M3 - Article

C2 - 18369266

AN - SCOPUS:41549136621

VL - 14

SP - 590

EP - 602

JO - IEEE Transactions on Visualization and Computer Graphics

JF - IEEE Transactions on Visualization and Computer Graphics

SN - 1077-2626

IS - 3

M1 - 4407698

ER -