Subscribe
Issue No.10 - October (2011 vol.17)
pp: 1420-1432
Tai Meng , Simon Fraser University, Burnaby
Alireza Entezari , University of Florida, Gainesville
Benjamin Smith , Simon Fraser University, Burnaby
Torsten Möller , Simon Fraser University, Burnaby
Daniel Weiskopf , Universität Stuttgart, Stuttgart
Arthur E. Kirkpatrick , Simon Fraser University, Burnaby
ABSTRACT
The Body-Centered Cubic (BCC) and Face-Centered Cubic (FCC) lattices have been analytically shown to be more efficient sampling lattices than the traditional Cartesian Cubic (CC) lattice, but there has been no estimate of their visual comparability. Two perceptual studies (each with N=12 participants) compared the visual quality of images rendered from BCC and FCC lattices to images rendered from the CC lattice. Images were generated from two signals: the commonly used Marschner-Lobb synthetic function and a computed tomography scan of a fish tail. Observers found that BCC and FCC could produce images of comparable visual quality to CC, using 30-35 percent fewer samples. For the images used in our studies, the L_2 error metric shows high correlation with the judgement of human observers. Using the L_2 metric as a proxy, the results of the experiments appear to extend across a wide range of images and parameter choices.
INDEX TERMS
Visual comparability, perceptual quality, 3D regular sampling and reconstruction, cartesian cubic (CC) lattice, body-centered cubic (BCC) lattice, face-centered cubic (FCC) lattice.
CITATION
Tai Meng, Alireza Entezari, Benjamin Smith, Torsten Möller, Daniel Weiskopf, Arthur E. Kirkpatrick, "Visual Comparability of 3D Regular Sampling and Reconstruction", IEEE Transactions on Visualization & Computer Graphics, vol.17, no. 10, pp. 1420-1432, October 2011, doi:10.1109/TVCG.2010.234
REFERENCES
 [1] S. Daly, “Digital Images and Human Vision,” The Visible Differences Predictor: An Algorithm for the Assessment of Image Fidelity, A.B. Watson, ed., pp. 179-206, MIT Press, 1993. [2] B. Efron, “Better Bootstrap Confidence Interval,” J. Am. Statistical Assoc., vol. 82, no. 397, pp. 171-185, 1987. [3] K. Engel, M. Hadwiger, J.M. Kniss, C. Rezk-Salama, and D. Weiskopf, Real-Time Volume Graphics. A.K. Peters, 2006. [4] A. Entezari, “Optimal Sampling Lattices and Trivariate Box Splines,” PhD thesis, Simon Fraser Univ., July 2007. [5] A. Entezari, R. Dyer, and T. Möller, “Linear and Cubic Box Splines for the Body Centered Cubic Lattice,” Proc. IEEE Visualization '04, pp. 11-18, 2004. [6] A. Entezari, T. Meng, S. Bergner, and T. Möller, “A Granular Three Dimensional Multiresolution Transform,” Proc. Eurographics/IEEE VGTC Symp. Visualization (EuroVis '06), pp. 267-274, 2006. [7] A. Entezari, D. Van De Ville, and T. Möller, “Practical Box Splines for Volume Rendering on the Body Centered Cubic Lattice,” IEEE Trans. Visualization and Computer Graphics, vol. 14, no. 2, pp. 313-328, Mar./Apr. 2008. [8] G. Farin, Curves and Surfaces for Computer-Aided Geometric Design, fourth ed. Academic Press, Inc., 1997. [9] A. Gaddipatti, R. Machiraju, and R. Yagel, “Steering Image Generation with Wavelet Based Perceptual Metric,” Computer Graphics Forum, vol. 16, no. 3, pp. 241-252, 1997. [10] A. Kaufman and K. Mueller, “Overview of Volume Rendering,” The Visualization Handbook, C.D. Hansen and C.R. Johnson, eds., pp. 127-174, Elsevier Butterworth-Heinemann, 2005. [11] J. Kovačević and M. Vetterli, “FCO Sampling of Digital Video Using Perfect Reconstruction Filter Banks,” IEEE Trans. Image Processing, vol. 2, no. 1, pp. 118-122, Jan. 1993. [12] H.R. Künsch, E. Agrell, and F.A. Hamprecht, “Optimal Lattices for Sampling,” IEEE Trans. Information Theory, vol. 51, no. 2, pp. 634-647, Feb. 2005. [13] J. Lubin, “Digital Images and Human Vision,” The Use of Psychophysical Data and Models in the Analysis of Display System Performance, A.B. Watson, ed., pp. 163-178, MIT Press, 1993. [14] S.R. Marschner and R.J. Lobb, “An Evaluation of Reconstruction Filters for Volume Rendering,” Proc. IEEE Visualization '94, pp. 100-107, 1994. [15] P. Marziliano, F. Dufaux, S. Winkler, and T. Ebrahimi, “Perceptual Blur and Ringing Metrics: Application to JPEG2000,” Signal Processing: Image Comm., vol. 19, pp. 163-172, 2004. [16] T. Meng, B. Smith, A. Entezari, A.E. Kirkpatrick, D. Weiskopf, L. Kalantari, and T. Möller, “On Visual Quality of Optimal 3D Sampling and Reconstruction,” Proc. Graphics Interface '07, pp. 265-272, 2007. [17] D.P. Mitchell and A.N. Netravali, “Reconstruction Filters in Computer Graphics,” Computer Graphics, vol. 22, no. 4, pp. 221-228, 1988. [18] A.V. Oppenheim, R.W. Schafer, and J.R. Buck, Discrete-Time Signal Processing, second ed. Prentice Hall, 1999. [19] D.P. Petersen and D. Middleton, “Sampling and Reconstruction of Wave-Number-Limited Functions in $N$ -Dimensional Euclidean Spaces,” Information and Control, vol. 5, no. 4, pp. 279-323, Dec. 1962. [20] D.F. Rogers, An Introduction to NURBS: With Historical Perspective. Morgan Kaufmann Publishers, 2001. [21] M. Unser, “Sampling—50 Years after Shannon,” Proc. IEEE, vol. 88, no. 4, pp. 569-587, Apr. 2000. [22] F.A. Wichmann and N.J. Hill, “The Psychometric Function: I. Fitting, Sampling, and Goodness of Fit,” Perception and Psychophysics, vol. 8, no. 63, pp. 1293-1313, 2001. [23] F.A. Wichmann and N.J. Hill, “The Psychometric Function: II. Bootstrap-Based Confidence Intervals and Sampling,” Perception and Psychophysics, vol. 8, no. 63, pp. 1314-1329, 2001.
30 ms
(Ver 2.0)

Marketing Automation Platform