This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
A Statistical Approach to Volume Data Quality Assessment
May/June 2008 (vol. 14 no. 3)
pp. 590-602
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.

[1] C.L. Bajaj, S. Park, and I. Ihm, “Visualization-Specific Compression of Large Volume Data,” Proc. Pacific Graphics (PG '01), pp.212-222, 2001.
[2] A.C. Bovik, Handbook of Image and Video Processing, second ed. Academic Press, 2005.
[3] R.W. Buccigrossi and E.P. Simoncelli, “Image Compression via Joint Statistical Characterization in the Wavelet Domain,” IEEE Trans. Image Processing, vol. 8, no. 12, pp. 1688-1701, 1999.
[4] J.G. Daugman, “Two-Dimensional Spectral Analysis of Cortical Receptive Field Profiles,” Vision Research, vol. 20, pp. 847-856, 1980.
[5] M.N. Do and M. Vetterli, “Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance,” IEEE Trans. Image Processing, vol. 11, no. 2, pp. 146-158, 2002.
[6] A. Gaddipati, R. Machiraju, and R. Yagel, “Steering Image Generation with Wavelet Based Perceptual Metric,” Computer Graphics Forum, vol. 16, no. 3, pp. 241-251, 1997.
[7] R.C. Gonzalez and R.E. Woods, Digital Image Processing, second ed. Prentice Hall, 2002.
[8] S. Guthe, M. Wand, J. Gonser, and W. Straßer, “Interactive Rendering of Large Volume Data Sets,” Proc. IEEE Visualization Conf. (VIS '02), pp. 53-60, 2002.
[9] C.E. Jacobs, A. Finkelstein, and D.H. Salesin, “Fast Multiresolution Image Querying,” Proc. ACM SIGGRAPH '95, pp. 277-286, 1995.
[10] K. Kim, C.M. Wittenbrink, and A. Pang, “Extended Specifications and Test Data Sets for Data Level Comparisons of Direct Volume Rendering Algorithms,” IEEE Trans. Visualization and Computer Graphics, vol. 7, no. 4, pp. 299-317, Oct.-Dec. 2001.
[11] T.-Y. Kim and Y.G. Shin, “An Efficient Wavelet-Based Compression Method for Volume Rendering,” Proc. Pacific Graphics (PG'99), pp. 147-157, 1999.
[12] L. Linsen, V. Pascucci, M.A. Duchaineau, B. Hamann, and K.I. Joy, “Hierarchical Representation of Time-Varying Volume Data with $\root{4}\of{2}$ Subdivision and Quadrilinear B-Spline Wavelets,” Proc. Pacific Graphics (PG '02), pp. 346-355, 2002.
[13] J. Luo and C.W. Chen, “Coherently Three-Dimensional Wavelet-Based Approach to Volumetric Image Compression,” J. Electronic Imaging, vol. 7, no. 3, pp. 474-485, 1998.
[14] S. Mallat, “A Theory for Multiresolution Signal Decomposition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674-693, July 1989.
[15] S. Muraki, “Volume Data and Wavelet Transforms,” IEEE Computer Graphics and Applications, vol. 13, no. 4, pp. 50-56, July/Aug. 1993.
[16] H.V. Poor, An Introduction to Signal Estimation and Detection, second ed. Springer, 1994.
[17] J. Portilla, V. Strela, M.J. Wainwright, and E.P. Simoncelli, “Image Denoising Using Scale Mixtures of Gaussians in the Wavelet Domain,” IEEE Trans. Image Processing, vol. 12, no. 11, pp. 1338-1351, 2003.
[18] N. Sahasrabudhe, J.E. West, R. Machiraju, and M. Janus, “Structured Spatial Domain Image and Data Comparison Metrics,” Proc. IEEE Visualization Conf. (VIS '99), pp. 97-104, 1999.
[19] A. Said and W.A. Pearlman, “A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees,” IEEE Trans. Circuits and Systems for Video Technology, vol. 6, no. 3, pp.243-250, 1996.
[20] J.M. Shapiro, “Embedded Image Coding Using Zerotrees of Wavelet Coefficients,” IEEE Trans. Signal Processing, vol. 41, no. 12, pp. 3445-3462, 1993.
[21] R. Shapley and P. Lennie, “Spatial Frequency Analysis in the Visual System,” Ann. Rev. of Neuroscience, vol. 8, pp. 547-583, 1985.
[22] E.P. Simoncelli and E.H. Adelson, “Noise Removal via Bayesian Wavelet Coring,” Proc. Int'l Conf. Image Processing (ICIP '96), pp.16-19, 1996.
[23] B.-S. Sohn, C.L. Bajaj, and V. Siddavanahalli, “Feature Based Volumetric Video Compression for Interactive Playback,” Proc. IEEE Symp. Volume Visualization, pp. 89-96, 2002.
[24] E.J. Stollnitz and D.H. Salesin, Wavelets for Computer Graphics: Theory and Applications. Morgan Kaufmann, 1996.
[25] G. Van de Wouwer, P. Scheunders, and D. Van Dyck, “Statistical Texture Characterization from Discrete Wavelet Representations,” IEEE Trans. Image Processing, vol. 8, no. 4, pp. 592-598, 1999.
[26] C. Wang, A. Garcia, and H.-W. Shen, “Interactive Level-of-Detail Selection Using Image-Based Quality Metric for Large Volume Visualization,” IEEE Trans. Visualization and Computer Graphics, vol. 13, no. 1, pp. 122-134, Jan./Feb. 2007.
[27] Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Trans. Image Processing, vol. 13, no. 4, pp. 600-612, 2004.
[28] Z. Wang, G. Wu, H.R. Sheikh, E.-H. Yang, and A.C. Bovik, “Quality-Aware Images,” IEEE Trans. Image Processing, vol. 15, no. 6, pp. 1680-1689, 2006.
[29] H. Zhou, M. Chen, and M.F. Webster, “Comparative Evaluation of Visualization and Experimental Results Using Image Comparison Metrics,” Proc. IEEE Visualization Conf. (VIS '02), pp. 315-322, 2002.

Index Terms:
Volume visualization, Statistical computing
Citation:
Chaoli Wang, Kwan-Liu Ma, "A Statistical Approach to Volume Data Quality Assessment," IEEE Transactions on Visualization and Computer Graphics, vol. 14, no. 3, pp. 590-602, May-June 2008, doi:10.1109/TVCG.2007.70628
Usage of this product signifies your acceptance of the Terms of Use.