Abstract

We compared the ability of three model observers (nonprewhitening matched filter with an eye filter, Hotelling and channelized Hotelling) in predicting the effect of JPEG and wavelet-Crewcode image compression on human visual detection of a simulated lesion in single frame digital x-ray coronary angiograms. All three model observers predicted the JPEG superiority present in human performance, although the nonprewhitening matched filter with an eye filter (NPWE) and the channelized Hotelling models were better predictors than the Hotelling model. The commonly used root mean square error and related peak signal to noise ratio metrics incorrectly predicted a JPEG inferiority. A particular image discrimination/perceptual difference model correctly predicted a JPEG advantage at low compression ratios but incorrectly predicted a JPEG inferiority at high compression ratios. In the second part of the paper, the NPWE model was used to perform automated simulated annealing optimization of the quantization matrix of the JPEG algorithm at 25:1 compression ratio. A subsequent psychophysical study resulted in improved human detection performance for images compressed with the NPWE optimized quantization matrix over the JPEG default quantization matrix. Together, our results show how model observers can be successfully used to perform automated evaluation and optimization of diagnostic performance in clinically relevant visual tasks using real anatomic backgrounds.

© 2003 Optical Society of America

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2001 (2)

A.E. Burgess, F.L. Jacobson, and P.F. Judy. “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419–437 (2001).
[CrossRef] [PubMed]

C.K. Abbey and H.H. Barrett. “Human and model-observer performance in ramp-spectrum noise: effects of regularization and object variability,” J. Opt. Soc. of Am. A 18, 473–488 (2001).
[CrossRef]

2000 (2)

M.P. Eckstein, C.K. Abbey, and F.O. Bochud. “Visual signal detection in structured backgrounds IV. Figures of merit for model observers with internal response,” J. Opt. Soc. Am. 17, 2 206–217 (2000).
[CrossRef]

R.A. Kerensky, J.T. Cusma, P. Kubilis, R. Simon, T.M. Bashore, J.W. Hirshfeld, D.R. Holmes Jr, C.J. Pepine, and S.E. Nissen, “American College of Cardiology/European Society of Cardiology International Study of Angiographic Data Compression Phase I: The effect of lossy data compression on recognition of diagnostic features in digital coronary angiography,” J. Am. College Cardiology 35, 1370–1379 (2000).
[CrossRef]

1999 (1)

1998 (2)

B. Zhao, L.H. Schwarz, and P.K. Kijewski. “Effect of lossy compression on lesion detection: Predictions of the nonprewhitening matched filter,” Med. Phys. 25, 1621–1624 (1998).
[CrossRef] [PubMed]

M.P. Eckstein, C.A. Abbey, and J.S. Whiting. “Human vs model observers in anatomic backgrounds,” Proceedings SPIE Image Perception 3340, 15–26 (1998).

1997 (4)

M.A. Webster and E. Miyahara. “Contrast adaptation and the spatial structure of natural images,” J. Opt. Soc. Am. A 9, 2355–2366 (1997).
[CrossRef]

W.A. Baker et al., “Lossy (15:1) JPEG compression of digital coronary angiograms does not limit detection of subtle morphological features,” Circ. 96, 1157–1164 (1997).
[CrossRef]

S. Silber, R. Dorr, G. Zindler, H. Muhling, and T. Diebel, “Impact of various compression rates on interpretation of digital coronary angiograms,” Int. J. Cardiology 60, 195–200 (1997).
[CrossRef]

A.E. Burgess, X. Li, and C.K. Abbey. “Visual signal detectability with two noise components: anomalous masking effects,” J. Opt. Soc. Am. A 14, 2420–2442 (1997).
[CrossRef]

1996 (2)

C.K. Abbey, H.H. Barrett, and D.W. Wilson. “Observer signal to noise ratios for the ML-EM algorithm,” Proc. SPIE 2712:47–58 (1996).
[CrossRef]

V. H. Rigolin, P. A. Robiolio, L.A. Spero, B.P. Harrawood, K.G. Morris, D.F. Fortin, W.A. Baker, T.M. Bashore, and J.T. Cusma, “Compression of Digital Coronary Angiograms Does Not Affect Visual or Quantitative Assessment of Coronary Artery Stenosis Severity,” Am. J. of Card. 78, 131–135 (1996).
[CrossRef]

1995 (1)

M.P. Eckstein and J.S. Whiting. “Lesion detection in structured noise,” Academic Radiology 2, 249–253 (1995).
[CrossRef] [PubMed]

1994 (1)

1993 (3)

H.H. Barrett, J. Yao, J.P. Rolland, and K.J. Myers. “Model observers for assessment of image quality,” Proc. Natl. Acad. Sci. USA 90:9758–9765 (1993).
[CrossRef] [PubMed]

H.A. Peterson, A.J. Ahumada, and A.B. Watson. “The visibility of DCT quantization noise, Soc. For Information Display,” Digest of Tech. Papers 24, 942–945 (1993).

A.B. Watson, “DCTune: A Technique for visual optimization of DCT quantization matrices for individual images, Soc. For Information Display,” Digest of Tech. Papers XXIV, 946–949 (1993).

1992 (3)

J.S. Whiting, M.P. Eckstein, S. Einav, and N.L. Eigler, “Perceptual Evaluation of JPEG compression for medical image sequences,” in OSA Annual Meeting Tech. Dig. 23, 161 (1992).

J. Yao and H.H. Barrett. “Predicting human performance by a channelized Hotelling observer model,” Math. Methods Med. Imaging, SPIE 1768:161–168 (1992).

J.P. Rolland and H.H. Barrett. “Effect of random inhomogeneity on observer detection performance,” J. Opt. Soc. Am. A 9, 649–658 (1992).
[CrossRef] [PubMed]

1988 (1)

1987 (1)

K. Myers and H.H. Barrett. “Addition of a channel mechanism to the ideal observer model,” J Opt. Soc. Am. A 4, 2447–2457 (1987).
[CrossRef] [PubMed]

1985 (2)

P.F. Judy and R.G. Swensson. “Detection of small focal lesions in CT images: effects of reconstruction filters and visual display windows,” British Journal of Radiology 58, 137–145 (1985).
[CrossRef] [PubMed]

K.J. Myers, H.H. Barrett, M.C. Borgstrom, D.D. Patton, and G.W. Seeley. “Effect of noise correlation on detectability of disk signals in medical imaging,” J. Opt. Soc. Am. A 2, 1752–1759 (1985).
[CrossRef] [PubMed]

1984 (2)

A.E. Burgess and H. Ghandeharian, “Visual signal detection. II. Signal location identification,” J. Opt. Soc. Am. A 1, 900–905 (1984).
[CrossRef] [PubMed]

M. Ishida, K. Doi, L.N. Loo, C.E. Metz, and J.L. Lehr. “Digital image processing: effect of detectability of simulated low-contrast radiographic patterns,” Radiology 150, 569–575 (1984).
[PubMed]

1981 (2)

R.G. Swensson and P.F. Judy. “Detection of noisy visual targets: model for the effects of spatial uncertainty and signal to noise ratio,” Percept. Psychophys. 29: 521–534 (1981).
[CrossRef] [PubMed]

Burgess AE, Wagner RB, Jennings RJ, and Barlow HB. “Efficiency of human visual signal discrimination,” Science 214: 93–94 (1981).
[CrossRef]

1980 (1)

S. Marcelja. “Mathematical description of the responses of simple cortical cells,” J. Opt. Soc. Am. A 70, 1297–1300 (1980).
[CrossRef]

Abbey, C.A.

M.P. Eckstein, C.A. Abbey, and J.S. Whiting. “Human vs model observers in anatomic backgrounds,” Proceedings SPIE Image Perception 3340, 15–26 (1998).

Abbey, C.K

M.P. Eckstein and C.K Abbey. “Model observers for signal known statistically tasks,” Proc. SPIE, Medical Imaging, Image Percep. and Performance, Ed. E.A. Krupinski and D.P. Chakraborty4321, 91–102 (2001).
[CrossRef]

Abbey, C.K.

C.K. Abbey and H.H. Barrett. “Human and model-observer performance in ramp-spectrum noise: effects of regularization and object variability,” J. Opt. Soc. of Am. A 18, 473–488 (2001).
[CrossRef]

M.P. Eckstein, C.K. Abbey, and F.O. Bochud. “Visual signal detection in structured backgrounds IV. Figures of merit for model observers with internal response,” J. Opt. Soc. Am. 17, 2 206–217 (2000).
[CrossRef]

A.E. Burgess, X. Li, and C.K. Abbey. “Visual signal detectability with two noise components: anomalous masking effects,” J. Opt. Soc. Am. A 14, 2420–2442 (1997).
[CrossRef]

C.K. Abbey, H.H. Barrett, and D.W. Wilson. “Observer signal to noise ratios for the ML-EM algorithm,” Proc. SPIE 2712:47–58 (1996).
[CrossRef]

F.O. Bochud, C.K. Abbey, and M.P. Eckstein. “Correlated human responses for visual detection in natural images; Annual Meeting of the Association for Research,” in Vision and Ophthalmology; Fort Lauderdale, USA; 40, 4; 350 (1999).

M.P. Eckstein, C.K. Abbey, and B. Pham. “The effect of image compression on signal known statistically tasks,” Proc. SPIE, Medical Imaging, Image Percep . and Performance, Ed. E.A. Krupinski and D.P. Chakraborty4686,13–24 (2002).
[CrossRef]

M.P. Eckstein, C.K. Abbey, and F.O Bochud. “Practical guide to model observers in synthetic and real noisy backgrounds,” in Handbook of Medical Imaging Vol. I: Physics and Psychophysics, Editors, J. Beutel and Van Metter Kundel, SPIE Press, 593–628 (2000).

C.K. Abbey, H.H. Barrett, and M.P. Eckstein. “Practical issues and methodology in assessment of image quality using model observers,” in Medical Imaging, H. Roerhig, ed., Proc. SPIE, The physics of medical imaging, 3032: 182–194 (1997).
[CrossRef]

H.H. Barrett, C.K. Abbey, B. Gallas, and M.P. Eckstein. “Stabilized estimates of Hotelling-observer detection performance in patient structured noise,” Proc. SPIE3340 (1998).
[CrossRef]

AE, Burgess

Burgess AE, Wagner RB, Jennings RJ, and Barlow HB. “Efficiency of human visual signal discrimination,” Science 214: 93–94 (1981).
[CrossRef]

Ahumada, A.J.

H.A. Peterson, A.J. Ahumada, and A.B. Watson. “The visibility of DCT quantization noise, Soc. For Information Display,” Digest of Tech. Papers 24, 942–945 (1993).

A.B. Watson, A.P. Gale, J.A. Solomon, and A.J. Ahumada. “Visibility of DCT quantization noise: Effects of display resolution, Proceedings, Society for Information Display,” San Jose, CA, Society for Information Display, 697–700 (1995).

Ahumada Jr., A.J.

A.J. Ahumada Jr., A.B. Watson, and A.M. Rohally. “Models of human image discrimination predict object detection in natural backgrounds,” in Human Vision, Visual Proc., and Digital Display VI, ed. B. Rogowitz and J. Allebach, SPIE2411, 355–362 (1995).
[CrossRef]

Allen, J.

A. Zandi, J. Allen, E.L. Schwartz, and M. Boliek, “Crewcode Lossless/Lossy Medical Image Compression,” IEEE Data Compression Conference, 212–221 (1995).

Baker, W.A.

W.A. Baker et al., “Lossy (15:1) JPEG compression of digital coronary angiograms does not limit detection of subtle morphological features,” Circ. 96, 1157–1164 (1997).
[CrossRef]

V. H. Rigolin, P. A. Robiolio, L.A. Spero, B.P. Harrawood, K.G. Morris, D.F. Fortin, W.A. Baker, T.M. Bashore, and J.T. Cusma, “Compression of Digital Coronary Angiograms Does Not Affect Visual or Quantitative Assessment of Coronary Artery Stenosis Severity,” Am. J. of Card. 78, 131–135 (1996).
[CrossRef]

Barrett, H.H.

C.K. Abbey and H.H. Barrett. “Human and model-observer performance in ramp-spectrum noise: effects of regularization and object variability,” J. Opt. Soc. of Am. A 18, 473–488 (2001).
[CrossRef]

C.K. Abbey, H.H. Barrett, and D.W. Wilson. “Observer signal to noise ratios for the ML-EM algorithm,” Proc. SPIE 2712:47–58 (1996).
[CrossRef]

H.H. Barrett, J. Yao, J.P. Rolland, and K.J. Myers. “Model observers for assessment of image quality,” Proc. Natl. Acad. Sci. USA 90:9758–9765 (1993).
[CrossRef] [PubMed]

J. Yao and H.H. Barrett. “Predicting human performance by a channelized Hotelling observer model,” Math. Methods Med. Imaging, SPIE 1768:161–168 (1992).

J.P. Rolland and H.H. Barrett. “Effect of random inhomogeneity on observer detection performance,” J. Opt. Soc. Am. A 9, 649–658 (1992).
[CrossRef] [PubMed]

K. Myers and H.H. Barrett. “Addition of a channel mechanism to the ideal observer model,” J Opt. Soc. Am. A 4, 2447–2457 (1987).
[CrossRef] [PubMed]

K.J. Myers, H.H. Barrett, M.C. Borgstrom, D.D. Patton, and G.W. Seeley. “Effect of noise correlation on detectability of disk signals in medical imaging,” J. Opt. Soc. Am. A 2, 1752–1759 (1985).
[CrossRef] [PubMed]

C.K. Abbey, H.H. Barrett, and M.P. Eckstein. “Practical issues and methodology in assessment of image quality using model observers,” in Medical Imaging, H. Roerhig, ed., Proc. SPIE, The physics of medical imaging, 3032: 182–194 (1997).
[CrossRef]

H.H. Barrett, C.K. Abbey, B. Gallas, and M.P. Eckstein. “Stabilized estimates of Hotelling-observer detection performance in patient structured noise,” Proc. SPIE3340 (1998).
[CrossRef]

Bartroff, J.L.

Bashore, T.M.

R.A. Kerensky, J.T. Cusma, P. Kubilis, R. Simon, T.M. Bashore, J.W. Hirshfeld, D.R. Holmes Jr, C.J. Pepine, and S.E. Nissen, “American College of Cardiology/European Society of Cardiology International Study of Angiographic Data Compression Phase I: The effect of lossy data compression on recognition of diagnostic features in digital coronary angiography,” J. Am. College Cardiology 35, 1370–1379 (2000).
[CrossRef]

V. H. Rigolin, P. A. Robiolio, L.A. Spero, B.P. Harrawood, K.G. Morris, D.F. Fortin, W.A. Baker, T.M. Bashore, and J.T. Cusma, “Compression of Digital Coronary Angiograms Does Not Affect Visual or Quantitative Assessment of Coronary Artery Stenosis Severity,” Am. J. of Card. 78, 131–135 (1996).
[CrossRef]

Bochud, F.O

M.P. Eckstein, C.K. Abbey, and F.O Bochud. “Practical guide to model observers in synthetic and real noisy backgrounds,” in Handbook of Medical Imaging Vol. I: Physics and Psychophysics, Editors, J. Beutel and Van Metter Kundel, SPIE Press, 593–628 (2000).

Bochud, F.O.

M.P. Eckstein, C.K. Abbey, and F.O. Bochud. “Visual signal detection in structured backgrounds IV. Figures of merit for model observers with internal response,” J. Opt. Soc. Am. 17, 2 206–217 (2000).
[CrossRef]

F.O. Bochud, C.K. Abbey, and M.P. Eckstein. “Correlated human responses for visual detection in natural images; Annual Meeting of the Association for Research,” in Vision and Ophthalmology; Fort Lauderdale, USA; 40, 4; 350 (1999).

Boliek, M.

A. Zandi, J. Allen, E.L. Schwartz, and M. Boliek, “Crewcode Lossless/Lossy Medical Image Compression,” IEEE Data Compression Conference, 212–221 (1995).

Borgstrom, M.C.

Burgess, A.E.

Chakraborty, D.

J.P. Johnson, J. Lubin, J. Nafziger, and D. Chakraborty. “Visual Discrimination Modeling of lesion discriminability,” Medical Imaging, Image Percep.and Performance, Ed. E.A. Krupinski and D.P. Chakraborty, Proc. SPIE, 4686, 248–255 (2002).
[CrossRef]

Chan, K.K.

K.K. Chan, C.C. Lau, S.L. Lou, A. Hayrepatian, B.K.T. Ho, and H.K. Huang, “Three-dimensional Transform Compression of Image from Dynamic Studies,” Medical Imaging IV: Image Capture and Display, Proc SPIE1232, 322–326 (1990).
[CrossRef]

Colborne, B.

Cusma, J.T.

R.A. Kerensky, J.T. Cusma, P. Kubilis, R. Simon, T.M. Bashore, J.W. Hirshfeld, D.R. Holmes Jr, C.J. Pepine, and S.E. Nissen, “American College of Cardiology/European Society of Cardiology International Study of Angiographic Data Compression Phase I: The effect of lossy data compression on recognition of diagnostic features in digital coronary angiography,” J. Am. College Cardiology 35, 1370–1379 (2000).
[CrossRef]

V. H. Rigolin, P. A. Robiolio, L.A. Spero, B.P. Harrawood, K.G. Morris, D.F. Fortin, W.A. Baker, T.M. Bashore, and J.T. Cusma, “Compression of Digital Coronary Angiograms Does Not Affect Visual or Quantitative Assessment of Coronary Artery Stenosis Severity,” Am. J. of Card. 78, 131–135 (1996).
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S. Daly, “The visible differences predictor: an algorithm for the assessment of image fidelity,” in Digital images and Human Vision, A.B. Watson, ed. (MIT Press, Cambridge, Mass., 1993) 162–178.

Diebel, T.

S. Silber, R. Dorr, G. Zindler, H. Muhling, and T. Diebel, “Impact of various compression rates on interpretation of digital coronary angiograms,” Int. J. Cardiology 60, 195–200 (1997).
[CrossRef]

Doi, K.

M. Ishida, K. Doi, L.N. Loo, C.E. Metz, and J.L. Lehr. “Digital image processing: effect of detectability of simulated low-contrast radiographic patterns,” Radiology 150, 569–575 (1984).
[PubMed]

Dorr, R.

S. Silber, R. Dorr, G. Zindler, H. Muhling, and T. Diebel, “Impact of various compression rates on interpretation of digital coronary angiograms,” Int. J. Cardiology 60, 195–200 (1997).
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M.P. Eckstein, C.K. Abbey, and F.O. Bochud. “Visual signal detection in structured backgrounds IV. Figures of merit for model observers with internal response,” J. Opt. Soc. Am. 17, 2 206–217 (2000).
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C.A. Morioka, M.P. Eckstein, J.L. Bartroff, J. Hausleiter, and J.S. Whiting, “Observer performance for JPEG vs. wavelet image compression of x-ray coronary angiograms,” Opt. Express 5, 8–19 (1999), http://www.opticsexpress.org/abstract.cfm?URI=OPEX-5-1-8
[CrossRef] [PubMed]

M.P. Eckstein, C.A. Abbey, and J.S. Whiting. “Human vs model observers in anatomic backgrounds,” Proceedings SPIE Image Perception 3340, 15–26 (1998).

M.P. Eckstein and J.S. Whiting. “Lesion detection in structured noise,” Academic Radiology 2, 249–253 (1995).
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J.S. Whiting, M.P. Eckstein, S. Einav, and N.L. Eigler, “Perceptual Evaluation of JPEG compression for medical image sequences,” in OSA Annual Meeting Tech. Dig. 23, 161 (1992).

H.H. Barrett, C.K. Abbey, B. Gallas, and M.P. Eckstein. “Stabilized estimates of Hotelling-observer detection performance in patient structured noise,” Proc. SPIE3340 (1998).
[CrossRef]

M.P. Eckstein, C.K. Abbey, and B. Pham. “The effect of image compression on signal known statistically tasks,” Proc. SPIE, Medical Imaging, Image Percep . and Performance, Ed. E.A. Krupinski and D.P. Chakraborty4686,13–24 (2002).
[CrossRef]

M.P. Eckstein and C.K Abbey. “Model observers for signal known statistically tasks,” Proc. SPIE, Medical Imaging, Image Percep. and Performance, Ed. E.A. Krupinski and D.P. Chakraborty4321, 91–102 (2001).
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F.O. Bochud, C.K. Abbey, and M.P. Eckstein. “Correlated human responses for visual detection in natural images; Annual Meeting of the Association for Research,” in Vision and Ophthalmology; Fort Lauderdale, USA; 40, 4; 350 (1999).

M.P. Eckstein, C.K. Abbey, and F.O Bochud. “Practical guide to model observers in synthetic and real noisy backgrounds,” in Handbook of Medical Imaging Vol. I: Physics and Psychophysics, Editors, J. Beutel and Van Metter Kundel, SPIE Press, 593–628 (2000).

C.K. Abbey, H.H. Barrett, and M.P. Eckstein. “Practical issues and methodology in assessment of image quality using model observers,” in Medical Imaging, H. Roerhig, ed., Proc. SPIE, The physics of medical imaging, 3032: 182–194 (1997).
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J.S. Whiting, M.P. Eckstein, S. Einav, and N.L. Eigler, “Perceptual Evaluation of JPEG compression for medical image sequences,” in OSA Annual Meeting Tech. Dig. 23, 161 (1992).

Einav, S.

J.S. Whiting, M.P. Eckstein, S. Einav, and N.L. Eigler, “Perceptual Evaluation of JPEG compression for medical image sequences,” in OSA Annual Meeting Tech. Dig. 23, 161 (1992).

Fortin, D.F.

V. H. Rigolin, P. A. Robiolio, L.A. Spero, B.P. Harrawood, K.G. Morris, D.F. Fortin, W.A. Baker, T.M. Bashore, and J.T. Cusma, “Compression of Digital Coronary Angiograms Does Not Affect Visual or Quantitative Assessment of Coronary Artery Stenosis Severity,” Am. J. of Card. 78, 131–135 (1996).
[CrossRef]

Gale, A.P.

A.B. Watson, A.P. Gale, J.A. Solomon, and A.J. Ahumada. “Visibility of DCT quantization noise: Effects of display resolution, Proceedings, Society for Information Display,” San Jose, CA, Society for Information Display, 697–700 (1995).

Gallas, B.

H.H. Barrett, C.K. Abbey, B. Gallas, and M.P. Eckstein. “Stabilized estimates of Hotelling-observer detection performance in patient structured noise,” Proc. SPIE3340 (1998).
[CrossRef]

Ghandeharian, H.

Goldberg, M.

M. Goldberg, S. Panchanathan, and L.A. Wang. “Comparison of Lossy Techniques for Digitized Radiographic Images,” Medical Imaging IV: Image Capture, Formatting and Display, Proc. SPIE1987, 269–281 (1993).

Green, D.M.

D.M. Green and J.A. Swets. Signal Detection Theory and Psychophysics, (Wiley, NewYork, 1966).

Harrawood, B.P.

V. H. Rigolin, P. A. Robiolio, L.A. Spero, B.P. Harrawood, K.G. Morris, D.F. Fortin, W.A. Baker, T.M. Bashore, and J.T. Cusma, “Compression of Digital Coronary Angiograms Does Not Affect Visual or Quantitative Assessment of Coronary Artery Stenosis Severity,” Am. J. of Card. 78, 131–135 (1996).
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Hausleiter, J.

Hayrepatian, A.

K.K. Chan, C.C. Lau, S.L. Lou, A. Hayrepatian, B.K.T. Ho, and H.K. Huang, “Three-dimensional Transform Compression of Image from Dynamic Studies,” Medical Imaging IV: Image Capture and Display, Proc SPIE1232, 322–326 (1990).
[CrossRef]

HB, Barlow

Burgess AE, Wagner RB, Jennings RJ, and Barlow HB. “Efficiency of human visual signal discrimination,” Science 214: 93–94 (1981).
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Hirshfeld, J.W.

R.A. Kerensky, J.T. Cusma, P. Kubilis, R. Simon, T.M. Bashore, J.W. Hirshfeld, D.R. Holmes Jr, C.J. Pepine, and S.E. Nissen, “American College of Cardiology/European Society of Cardiology International Study of Angiographic Data Compression Phase I: The effect of lossy data compression on recognition of diagnostic features in digital coronary angiography,” J. Am. College Cardiology 35, 1370–1379 (2000).
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Ho, B.K.T.

K.K. Chan, C.C. Lau, S.L. Lou, A. Hayrepatian, B.K.T. Ho, and H.K. Huang, “Three-dimensional Transform Compression of Image from Dynamic Studies,” Medical Imaging IV: Image Capture and Display, Proc SPIE1232, 322–326 (1990).
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Holmes Jr, D.R.

R.A. Kerensky, J.T. Cusma, P. Kubilis, R. Simon, T.M. Bashore, J.W. Hirshfeld, D.R. Holmes Jr, C.J. Pepine, and S.E. Nissen, “American College of Cardiology/European Society of Cardiology International Study of Angiographic Data Compression Phase I: The effect of lossy data compression on recognition of diagnostic features in digital coronary angiography,” J. Am. College Cardiology 35, 1370–1379 (2000).
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Huang, H.K.

K.K. Chan, C.C. Lau, S.L. Lou, A. Hayrepatian, B.K.T. Ho, and H.K. Huang, “Three-dimensional Transform Compression of Image from Dynamic Studies,” Medical Imaging IV: Image Capture and Display, Proc SPIE1232, 322–326 (1990).
[CrossRef]

Ishida, M.

M. Ishida, K. Doi, L.N. Loo, C.E. Metz, and J.L. Lehr. “Digital image processing: effect of detectability of simulated low-contrast radiographic patterns,” Radiology 150, 569–575 (1984).
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A.E. Burgess, F.L. Jacobson, and P.F. Judy. “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419–437 (2001).
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J.P. Johnson, J. Lubin, J. Nafziger, and D. Chakraborty. “Visual Discrimination Modeling of lesion discriminability,” Medical Imaging, Image Percep.and Performance, Ed. E.A. Krupinski and D.P. Chakraborty, Proc. SPIE, 4686, 248–255 (2002).
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A.E. Burgess, F.L. Jacobson, and P.F. Judy. “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419–437 (2001).
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P.F. Judy and R.G. Swensson. “Detection of small focal lesions in CT images: effects of reconstruction filters and visual display windows,” British Journal of Radiology 58, 137–145 (1985).
[CrossRef] [PubMed]

R.G. Swensson and P.F. Judy. “Detection of noisy visual targets: model for the effects of spatial uncertainty and signal to noise ratio,” Percept. Psychophys. 29: 521–534 (1981).
[CrossRef] [PubMed]

Kerensky, R.A.

R.A. Kerensky, J.T. Cusma, P. Kubilis, R. Simon, T.M. Bashore, J.W. Hirshfeld, D.R. Holmes Jr, C.J. Pepine, and S.E. Nissen, “American College of Cardiology/European Society of Cardiology International Study of Angiographic Data Compression Phase I: The effect of lossy data compression on recognition of diagnostic features in digital coronary angiography,” J. Am. College Cardiology 35, 1370–1379 (2000).
[CrossRef]

Kijewski, P.K.

B. Zhao, L.H. Schwarz, and P.K. Kijewski. “Effect of lossy compression on lesion detection: Predictions of the nonprewhitening matched filter,” Med. Phys. 25, 1621–1624 (1998).
[CrossRef] [PubMed]

Kubilis, P.

R.A. Kerensky, J.T. Cusma, P. Kubilis, R. Simon, T.M. Bashore, J.W. Hirshfeld, D.R. Holmes Jr, C.J. Pepine, and S.E. Nissen, “American College of Cardiology/European Society of Cardiology International Study of Angiographic Data Compression Phase I: The effect of lossy data compression on recognition of diagnostic features in digital coronary angiography,” J. Am. College Cardiology 35, 1370–1379 (2000).
[CrossRef]

Lau, C.C.

K.K. Chan, C.C. Lau, S.L. Lou, A. Hayrepatian, B.K.T. Ho, and H.K. Huang, “Three-dimensional Transform Compression of Image from Dynamic Studies,” Medical Imaging IV: Image Capture and Display, Proc SPIE1232, 322–326 (1990).
[CrossRef]

Lehr, J.L.

M. Ishida, K. Doi, L.N. Loo, C.E. Metz, and J.L. Lehr. “Digital image processing: effect of detectability of simulated low-contrast radiographic patterns,” Radiology 150, 569–575 (1984).
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Lo, S.C.

S.C. Lo, E.L. Shen, and K.M. Seong, “An image splitting and remapping method for radiological image compression,” Medical Imaging IV: Image Capture and Display, Proc. SPIE1232, 312–321 (1990).
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M. Ishida, K. Doi, L.N. Loo, C.E. Metz, and J.L. Lehr. “Digital image processing: effect of detectability of simulated low-contrast radiographic patterns,” Radiology 150, 569–575 (1984).
[PubMed]

Lou, S.L.

K.K. Chan, C.C. Lau, S.L. Lou, A. Hayrepatian, B.K.T. Ho, and H.K. Huang, “Three-dimensional Transform Compression of Image from Dynamic Studies,” Medical Imaging IV: Image Capture and Display, Proc SPIE1232, 322–326 (1990).
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J.P. Johnson, J. Lubin, J. Nafziger, and D. Chakraborty. “Visual Discrimination Modeling of lesion discriminability,” Medical Imaging, Image Percep.and Performance, Ed. E.A. Krupinski and D.P. Chakraborty, Proc. SPIE, 4686, 248–255 (2002).
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S. Marcelja. “Mathematical description of the responses of simple cortical cells,” J. Opt. Soc. Am. A 70, 1297–1300 (1980).
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M. Ishida, K. Doi, L.N. Loo, C.E. Metz, and J.L. Lehr. “Digital image processing: effect of detectability of simulated low-contrast radiographic patterns,” Radiology 150, 569–575 (1984).
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W.B. Pennebaker and J.L. Mitchell, The JPEG still image data compression standard, (Van Nostrand Reinhold, New York, 1993).

Miyahara, E.

M.A. Webster and E. Miyahara. “Contrast adaptation and the spatial structure of natural images,” J. Opt. Soc. Am. A 9, 2355–2366 (1997).
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Morris, K.G.

V. H. Rigolin, P. A. Robiolio, L.A. Spero, B.P. Harrawood, K.G. Morris, D.F. Fortin, W.A. Baker, T.M. Bashore, and J.T. Cusma, “Compression of Digital Coronary Angiograms Does Not Affect Visual or Quantitative Assessment of Coronary Artery Stenosis Severity,” Am. J. of Card. 78, 131–135 (1996).
[CrossRef]

Muhling, H.

S. Silber, R. Dorr, G. Zindler, H. Muhling, and T. Diebel, “Impact of various compression rates on interpretation of digital coronary angiograms,” Int. J. Cardiology 60, 195–200 (1997).
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K. Myers and H.H. Barrett. “Addition of a channel mechanism to the ideal observer model,” J Opt. Soc. Am. A 4, 2447–2457 (1987).
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Nafziger, J.

J.P. Johnson, J. Lubin, J. Nafziger, and D. Chakraborty. “Visual Discrimination Modeling of lesion discriminability,” Medical Imaging, Image Percep.and Performance, Ed. E.A. Krupinski and D.P. Chakraborty, Proc. SPIE, 4686, 248–255 (2002).
[CrossRef]

Nissen, S.E.

R.A. Kerensky, J.T. Cusma, P. Kubilis, R. Simon, T.M. Bashore, J.W. Hirshfeld, D.R. Holmes Jr, C.J. Pepine, and S.E. Nissen, “American College of Cardiology/European Society of Cardiology International Study of Angiographic Data Compression Phase I: The effect of lossy data compression on recognition of diagnostic features in digital coronary angiography,” J. Am. College Cardiology 35, 1370–1379 (2000).
[CrossRef]

Panchanathan, S.

M. Goldberg, S. Panchanathan, and L.A. Wang. “Comparison of Lossy Techniques for Digitized Radiographic Images,” Medical Imaging IV: Image Capture, Formatting and Display, Proc. SPIE1987, 269–281 (1993).

Patton, D.D.

Pennebaker, W.B.

W.B. Pennebaker and J.L. Mitchell, The JPEG still image data compression standard, (Van Nostrand Reinhold, New York, 1993).

Pepine, C.J.

R.A. Kerensky, J.T. Cusma, P. Kubilis, R. Simon, T.M. Bashore, J.W. Hirshfeld, D.R. Holmes Jr, C.J. Pepine, and S.E. Nissen, “American College of Cardiology/European Society of Cardiology International Study of Angiographic Data Compression Phase I: The effect of lossy data compression on recognition of diagnostic features in digital coronary angiography,” J. Am. College Cardiology 35, 1370–1379 (2000).
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Peterson, H.A.

H.A. Peterson, A.J. Ahumada, and A.B. Watson. “The visibility of DCT quantization noise, Soc. For Information Display,” Digest of Tech. Papers 24, 942–945 (1993).

Pham, B.

M.P. Eckstein, C.K. Abbey, and B. Pham. “The effect of image compression on signal known statistically tasks,” Proc. SPIE, Medical Imaging, Image Percep . and Performance, Ed. E.A. Krupinski and D.P. Chakraborty4686,13–24 (2002).
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RB, Wagner

Burgess AE, Wagner RB, Jennings RJ, and Barlow HB. “Efficiency of human visual signal discrimination,” Science 214: 93–94 (1981).
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Rigolin, V. H.

V. H. Rigolin, P. A. Robiolio, L.A. Spero, B.P. Harrawood, K.G. Morris, D.F. Fortin, W.A. Baker, T.M. Bashore, and J.T. Cusma, “Compression of Digital Coronary Angiograms Does Not Affect Visual or Quantitative Assessment of Coronary Artery Stenosis Severity,” Am. J. of Card. 78, 131–135 (1996).
[CrossRef]

RJ, Jennings

Burgess AE, Wagner RB, Jennings RJ, and Barlow HB. “Efficiency of human visual signal discrimination,” Science 214: 93–94 (1981).
[CrossRef]

Robiolio, P. A.

V. H. Rigolin, P. A. Robiolio, L.A. Spero, B.P. Harrawood, K.G. Morris, D.F. Fortin, W.A. Baker, T.M. Bashore, and J.T. Cusma, “Compression of Digital Coronary Angiograms Does Not Affect Visual or Quantitative Assessment of Coronary Artery Stenosis Severity,” Am. J. of Card. 78, 131–135 (1996).
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Rohally, A.M.

A.J. Ahumada Jr., A.B. Watson, and A.M. Rohally. “Models of human image discrimination predict object detection in natural backgrounds,” in Human Vision, Visual Proc., and Digital Display VI, ed. B. Rogowitz and J. Allebach, SPIE2411, 355–362 (1995).
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Rolland, J.P.

H.H. Barrett, J. Yao, J.P. Rolland, and K.J. Myers. “Model observers for assessment of image quality,” Proc. Natl. Acad. Sci. USA 90:9758–9765 (1993).
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J.P. Rolland and H.H. Barrett. “Effect of random inhomogeneity on observer detection performance,” J. Opt. Soc. Am. A 9, 649–658 (1992).
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A. Zandi, J. Allen, E.L. Schwartz, and M. Boliek, “Crewcode Lossless/Lossy Medical Image Compression,” IEEE Data Compression Conference, 212–221 (1995).

Schwarz, L.H.

B. Zhao, L.H. Schwarz, and P.K. Kijewski. “Effect of lossy compression on lesion detection: Predictions of the nonprewhitening matched filter,” Med. Phys. 25, 1621–1624 (1998).
[CrossRef] [PubMed]

Seeley, G.W.

Seong, K.M.

S.C. Lo, E.L. Shen, and K.M. Seong, “An image splitting and remapping method for radiological image compression,” Medical Imaging IV: Image Capture and Display, Proc. SPIE1232, 312–321 (1990).
[CrossRef]

Shen, E.L.

S.C. Lo, E.L. Shen, and K.M. Seong, “An image splitting and remapping method for radiological image compression,” Medical Imaging IV: Image Capture and Display, Proc. SPIE1232, 312–321 (1990).
[CrossRef]

Silber, S.

S. Silber, R. Dorr, G. Zindler, H. Muhling, and T. Diebel, “Impact of various compression rates on interpretation of digital coronary angiograms,” Int. J. Cardiology 60, 195–200 (1997).
[CrossRef]

Simon, R.

R.A. Kerensky, J.T. Cusma, P. Kubilis, R. Simon, T.M. Bashore, J.W. Hirshfeld, D.R. Holmes Jr, C.J. Pepine, and S.E. Nissen, “American College of Cardiology/European Society of Cardiology International Study of Angiographic Data Compression Phase I: The effect of lossy data compression on recognition of diagnostic features in digital coronary angiography,” J. Am. College Cardiology 35, 1370–1379 (2000).
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W.E. Smith. “Simulated annealing and estimation theory in coded aperture imaging,” PhD Dissertation, University of Arizona (2002).

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A.B. Watson, A.P. Gale, J.A. Solomon, and A.J. Ahumada. “Visibility of DCT quantization noise: Effects of display resolution, Proceedings, Society for Information Display,” San Jose, CA, Society for Information Display, 697–700 (1995).

Spero, L.A.

V. H. Rigolin, P. A. Robiolio, L.A. Spero, B.P. Harrawood, K.G. Morris, D.F. Fortin, W.A. Baker, T.M. Bashore, and J.T. Cusma, “Compression of Digital Coronary Angiograms Does Not Affect Visual or Quantitative Assessment of Coronary Artery Stenosis Severity,” Am. J. of Card. 78, 131–135 (1996).
[CrossRef]

Swensson, R.G.

P.F. Judy and R.G. Swensson. “Detection of small focal lesions in CT images: effects of reconstruction filters and visual display windows,” British Journal of Radiology 58, 137–145 (1985).
[CrossRef] [PubMed]

R.G. Swensson and P.F. Judy. “Detection of noisy visual targets: model for the effects of spatial uncertainty and signal to noise ratio,” Percept. Psychophys. 29: 521–534 (1981).
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D.M. Green and J.A. Swets. Signal Detection Theory and Psychophysics, (Wiley, NewYork, 1966).

Wagner, R.F.

R.F. Wagner and K.E. Weaver. “An assortment of image quality indices for radiographic film-screen combinations- can they be resolved?” In Application of Optical Instrumentation in Medicine I, P.L. Carson, WH Hendee, and WC Zarnstorff, eds, Proc. SPIE35, 83–94 (1972).
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Wang, L.A.

M. Goldberg, S. Panchanathan, and L.A. Wang. “Comparison of Lossy Techniques for Digitized Radiographic Images,” Medical Imaging IV: Image Capture, Formatting and Display, Proc. SPIE1987, 269–281 (1993).

Watson, A.B.

H.A. Peterson, A.J. Ahumada, and A.B. Watson. “The visibility of DCT quantization noise, Soc. For Information Display,” Digest of Tech. Papers 24, 942–945 (1993).

A.B. Watson, “DCTune: A Technique for visual optimization of DCT quantization matrices for individual images, Soc. For Information Display,” Digest of Tech. Papers XXIV, 946–949 (1993).

A.B. Watson, A.P. Gale, J.A. Solomon, and A.J. Ahumada. “Visibility of DCT quantization noise: Effects of display resolution, Proceedings, Society for Information Display,” San Jose, CA, Society for Information Display, 697–700 (1995).

A.J. Ahumada Jr., A.B. Watson, and A.M. Rohally. “Models of human image discrimination predict object detection in natural backgrounds,” in Human Vision, Visual Proc., and Digital Display VI, ed. B. Rogowitz and J. Allebach, SPIE2411, 355–362 (1995).
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R.F. Wagner and K.E. Weaver. “An assortment of image quality indices for radiographic film-screen combinations- can they be resolved?” In Application of Optical Instrumentation in Medicine I, P.L. Carson, WH Hendee, and WC Zarnstorff, eds, Proc. SPIE35, 83–94 (1972).
[CrossRef]

Webster, M.A.

M.A. Webster and E. Miyahara. “Contrast adaptation and the spatial structure of natural images,” J. Opt. Soc. Am. A 9, 2355–2366 (1997).
[CrossRef]

Whiting, J.S.

C.A. Morioka, M.P. Eckstein, J.L. Bartroff, J. Hausleiter, and J.S. Whiting, “Observer performance for JPEG vs. wavelet image compression of x-ray coronary angiograms,” Opt. Express 5, 8–19 (1999), http://www.opticsexpress.org/abstract.cfm?URI=OPEX-5-1-8
[CrossRef] [PubMed]

M.P. Eckstein, C.A. Abbey, and J.S. Whiting. “Human vs model observers in anatomic backgrounds,” Proceedings SPIE Image Perception 3340, 15–26 (1998).

M.P. Eckstein and J.S. Whiting. “Lesion detection in structured noise,” Academic Radiology 2, 249–253 (1995).
[CrossRef] [PubMed]

J.S. Whiting, M.P. Eckstein, S. Einav, and N.L. Eigler, “Perceptual Evaluation of JPEG compression for medical image sequences,” in OSA Annual Meeting Tech. Dig. 23, 161 (1992).

Wilson, D.W.

C.K. Abbey, H.H. Barrett, and D.W. Wilson. “Observer signal to noise ratios for the ML-EM algorithm,” Proc. SPIE 2712:47–58 (1996).
[CrossRef]

Yao, J.

H.H. Barrett, J. Yao, J.P. Rolland, and K.J. Myers. “Model observers for assessment of image quality,” Proc. Natl. Acad. Sci. USA 90:9758–9765 (1993).
[CrossRef] [PubMed]

J. Yao and H.H. Barrett. “Predicting human performance by a channelized Hotelling observer model,” Math. Methods Med. Imaging, SPIE 1768:161–168 (1992).

Zandi, A.

A. Zandi, J. Allen, E.L. Schwartz, and M. Boliek, “Crewcode Lossless/Lossy Medical Image Compression,” IEEE Data Compression Conference, 212–221 (1995).

Zhao, B.

B. Zhao, L.H. Schwarz, and P.K. Kijewski. “Effect of lossy compression on lesion detection: Predictions of the nonprewhitening matched filter,” Med. Phys. 25, 1621–1624 (1998).
[CrossRef] [PubMed]

Zindler, G.

S. Silber, R. Dorr, G. Zindler, H. Muhling, and T. Diebel, “Impact of various compression rates on interpretation of digital coronary angiograms,” Int. J. Cardiology 60, 195–200 (1997).
[CrossRef]

Academic Radiology (1)

M.P. Eckstein and J.S. Whiting. “Lesion detection in structured noise,” Academic Radiology 2, 249–253 (1995).
[CrossRef] [PubMed]

Am. J. of Card. (1)

V. H. Rigolin, P. A. Robiolio, L.A. Spero, B.P. Harrawood, K.G. Morris, D.F. Fortin, W.A. Baker, T.M. Bashore, and J.T. Cusma, “Compression of Digital Coronary Angiograms Does Not Affect Visual or Quantitative Assessment of Coronary Artery Stenosis Severity,” Am. J. of Card. 78, 131–135 (1996).
[CrossRef]

British Journal of Radiology (1)

P.F. Judy and R.G. Swensson. “Detection of small focal lesions in CT images: effects of reconstruction filters and visual display windows,” British Journal of Radiology 58, 137–145 (1985).
[CrossRef] [PubMed]

Circ. (1)

W.A. Baker et al., “Lossy (15:1) JPEG compression of digital coronary angiograms does not limit detection of subtle morphological features,” Circ. 96, 1157–1164 (1997).
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Figures (5)

Fig. 1.
Fig. 1.

Human (red squares for JPEG, blue triangles for wavelet Crewcode) vs. model performance (empty squares and continuous line). Different rows are for different model observes (NPWE; Non-Prewhitening matched filter with an eye filter, CH-Hot: channelized Hotelling; HOT: Hotelling) as a function of compression ratio. Top line is for JPEG and bottom line is for wavelet Crewcode. Left and right columns non-physician observers: GR and CH.

Fig. 2.
Fig. 2.

Physician (red symbols, JPEG; blue symbols wavelet-Crewcode) vs. model observer performance. Different panels are for different model observers as a function of compression ratio.

Fig. 3.
Fig. 3.

Left: Root mean square error between original and image undergoing different degrees of compression (averaged across the 424 test images) for the JPEG (red squares) and wavelet-Crewcode algorithms (blue triangles). Right: DC-tune 2.0 metric as a function of image compression for the JPEG and wavelet-Crewcode algorithms.

Fig. 4:
Fig. 4:

Simulated Annealing procedure applied to the optimization of the JPEG quantization matrix based on model observer performance following Smith (1985)

Fig. 5.
Fig. 5.

Top left: Performance of the NPWE model for three different quantization matrices. Top right: Performance for NPWE model across compression ratios for the default quantization matrix and the 25:1 optimized quantization matrix. Bottom left: Performance for observer GR for three quantization matrices. Bottom right: Performance for physician observer DV for three quantization matrices

Tables (2)

Tables Icon

Table 1. Goodness of fit assessed with a reduced Chi-square (χr2), for the three models to individual data for JPEG, Crewcode-wavelet and pooled across both algorithms and observers. Highlighted numbers correspond to lowest reduced chi-square value within a condition. Reduced Chi-squared is defined as χ r 2 = 1 n p i = 0 n ( d h , i ' d m , i ' ) 2 σ i 2 , where d’h,i is for the human in the ith condition, d’m,i is for the model, σi2 is the observed variance of the human d’h,i, n is the number of data points (n=10) and p is the number of fitting parameters (p=1).

Tables Icon

Table 2. Quantization matrix for the default JPEG standard

Equations (14)

Equations on this page are rendered with MathJax. Learn more.

λ m = x = 1 N y = 1 N w x y g m x y
λ m = w t g m
w x y = FFT 1 [ s u v E u v 2 ]
E ( f ) = f ρ exp ( - cf γ )
w h = K - 1 [ < g s > - < g b > ]
V x y = exp [ 4 ln 2 ( x 2 + y 2 ) W s 2 ] cos [ 2 π f c ( x cos θ + y sin θ ) + β ]
b w = log 2 [ f c + 1 2 W f f c 1 2 W f ]
a = K V - 1 [ < g V s > - < g V b > ]
w x y = i = 0 N a i · V i x y
λ m = λ m , e + ε m
P ̂ c = 1 J j = 1 J step ( λ s , j max i ( λ b , ij ) )
Pc d mafc M = + φ ( z d mafc ) [ ϕ ( z ) ] M 1 dz
RMSE = 1 XY x = 0 X y = 0 Y [ I x y I c x y ] 2
c ' i , j = round ( c i , j q i , j )

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