Abstract

We demonstrate an image-compression technique that uses what we believe is a new noniterative codebook generation algorithm for vector quantization. The technique supports rapid decompression and is equally applicable to individual images or to a set of images without the need for interframe processing. Compression with a single-image codebook is tested on (1) ten confocal images of the hindbrain of a mouse embryo, (2) video images of a polystyrene microsphere that is manipulated by a focused laser light, and (3) five fluorescence images of the embryo eye lens taken at different magnifications. The reconstructions are assessed with the normalized mean-squared error and with Linfoot’s criteria of fidelity, structural content, and correlation quality. Experimental results with single-image compression show that the technique produces fewer local artifacts than JPEG compression, especially with noisy images. Results with video and confocal image series indicate that single-image codebook generation is sufficient at practical compression ratios for producing acceptable reconstructions for mouse embryo analysis and for viewing optically trapped microspheres. Experiments with the magnified images also reveal that the compression scheme is robust to scaling.

© 1999 Optical Society of America

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References

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    [CrossRef]
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    [CrossRef]
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    [CrossRef] [PubMed]
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    [CrossRef]
  8. S. Haykin, Neural Networks (Macmillan, New York, 1994).
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    [CrossRef]
  11. J. Bradley, C. Brislawn, “Image compression by vector quantization of multiresolution decomposition,” Physica D 60, 245–258 (1992).
    [CrossRef]
  12. J. Foley, Computer Graphics: Principles and Practice (Addison-Wesley, New York, 1990).
  13. J. Proakis, D. Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications, 2nd ed. (Macmillan, New York, 1992).
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
  22. N. Akrout, R. Prost, R. Goutte, “Image compression by vector quantization: a review focused on codebook generation,” Image Vision Comput. 12, 627–638 (1994).
    [CrossRef]
  23. M. Ting, E. Riskin, “Error-diffused image compression using a binary-to-gray-scale decoder and predictive pruned tree-structured vector quantization,” IEEE Trans. Image Process. 3, 854–857 (1994).
    [CrossRef] [PubMed]
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    [CrossRef]
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  34. M. Soriano, C. Saloma, “Cell classification by a learning principal component analyzer and a backpropagation neural network,” Bioimaging 3, 168–175 (1995).
    [CrossRef]
  35. M. Soriano, C. Saloma, “Improved classification robustness for noisy cell images represented as principal-component projections in a hybird recognition system,” Appl. Opt. 37, 3628–3639 (1998).
    [CrossRef]
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1998 (3)

1996 (1)

R. Zaciu, C. Lamba, G. Nicula, “Image compression using an overcomplete discrete wavelet transform,” IEEE Trans. Cons. Electron. 42, 800–807 (1996).
[CrossRef]

1995 (3)

B. Sherlock, M. Donald, “Fast discrete cosine transform,” ACM (Assoc. Comput. Mach.) Trans. Math. Software 21, 372–378 (1995).
[CrossRef]

W. Li, E. Salari, “A fast vector quantization encoding method for image compression,” IEEE Trans. Ciruits Syst. Video 5, 119–123 (1995).
[CrossRef]

M. Soriano, C. Saloma, “Cell classification by a learning principal component analyzer and a backpropagation neural network,” Bioimaging 3, 168–175 (1995).
[CrossRef]

1994 (4)

N. Akrout, R. Prost, R. Goutte, “Image compression by vector quantization: a review focused on codebook generation,” Image Vision Comput. 12, 627–638 (1994).
[CrossRef]

M. Ting, E. Riskin, “Error-diffused image compression using a binary-to-gray-scale decoder and predictive pruned tree-structured vector quantization,” IEEE Trans. Image Process. 3, 854–857 (1994).
[CrossRef] [PubMed]

V. Sitaram, C. Huang, P. Israelsen, “Efficient codebooks for vector quantization image compression with an adaptive tree search algorithm,” IEEE Trans. Commun. 42, 3027–3033 (1994).
[CrossRef]

P. Cosman, R. Gray, R. Olshen, “Evaluating quality of compressed medical images: SNR, subjective rating and diagnostic accuracy,” Proc. IEEE 82, 919–930 (1994).
[CrossRef]

1992 (3)

C. Huang, Q. Bi, G. Stiles, “Fast full search equivalent encoding algorithms for image compression using vector quantization,” IEEE Trans. Image Process. 1, 413–416 (1992).
[CrossRef] [PubMed]

J. Bradley, C. Brislawn, “Image compression by vector quantization of multiresolution decomposition,” Physica D 60, 245–258 (1992).
[CrossRef]

A. Lewis, G. Knowles, “Image compression using 2-D wavelet transform,” IEEE Trans. Image Process. 1, 244–249 (1992).
[CrossRef]

1991 (3)

D. Le Gall, “A video compression standard for multimedia applications,” Commun. ACM 32, 46–58 (1991).
[CrossRef]

G. Wallace, “The JPEG still picture compression standard,” Commun. ACM 32, 30–34 (1991).
[CrossRef]

S. Luttrell, “Code vector density in topographic mappings,” IEEE Trans. Neural Netw. 2, 427–436 (1991).
[CrossRef]

1990 (1)

T. Kohonen, “The self-organizing map,” Proc. IEEE 78, 1464–1480 (1990).
[CrossRef]

1988 (1)

S. Wan, S. Wong, P. Prusinkiewicz, “An algorithm for multidimensional data clustering,” ACM (Assoc. Comput. Mach.) Trans. Math. Software 14, 153–162 (1988).
[CrossRef]

1985 (1)

1965 (1)

J. Cooley, J. Tukey, “An algorithm for the machine calculation of complex Fourier series,” Math. Comput. 19, 297–301 (1965).
[CrossRef]

Akrout, N.

N. Akrout, R. Prost, R. Goutte, “Image compression by vector quantization: a review focused on codebook generation,” Image Vision Comput. 12, 627–638 (1994).
[CrossRef]

Barnsley, M.

M. Barnsley, L. Hurd, Fractal Image Compression (AK Peters, Wellesley, Mass., 1993).

Bi, Q.

C. Huang, Q. Bi, G. Stiles, “Fast full search equivalent encoding algorithms for image compression using vector quantization,” IEEE Trans. Image Process. 1, 413–416 (1992).
[CrossRef] [PubMed]

Bradley, J.

J. Bradley, C. Brislawn, “Image compression by vector quantization of multiresolution decomposition,” Physica D 60, 245–258 (1992).
[CrossRef]

Brislawn, C.

J. Bradley, C. Brislawn, “Image compression by vector quantization of multiresolution decomposition,” Physica D 60, 245–258 (1992).
[CrossRef]

Cooley, J.

J. Cooley, J. Tukey, “An algorithm for the machine calculation of complex Fourier series,” Math. Comput. 19, 297–301 (1965).
[CrossRef]

Cosman, P.

P. Cosman, R. Gray, R. Olshen, “Evaluating quality of compressed medical images: SNR, subjective rating and diagnostic accuracy,” Proc. IEEE 82, 919–930 (1994).
[CrossRef]

Donald, M.

B. Sherlock, M. Donald, “Fast discrete cosine transform,” ACM (Assoc. Comput. Mach.) Trans. Math. Software 21, 372–378 (1995).
[CrossRef]

Fales, C.

Foley, J.

J. Foley, Computer Graphics: Principles and Practice (Addison-Wesley, New York, 1990).

Gersho, A.

A. Gersho, R. Gray, Vector Quantization and Signal Compression (Kluwer Academic, Dordrecht, The Netherlands, 1992).
[CrossRef]

Gonzales, R.

R. Gonzales, R. Woods, Digital Image Processing (Addison-Wesley, New York, 1992).

Goutte, R.

N. Akrout, R. Prost, R. Goutte, “Image compression by vector quantization: a review focused on codebook generation,” Image Vision Comput. 12, 627–638 (1994).
[CrossRef]

Gray, R.

P. Cosman, R. Gray, R. Olshen, “Evaluating quality of compressed medical images: SNR, subjective rating and diagnostic accuracy,” Proc. IEEE 82, 919–930 (1994).
[CrossRef]

A. Gersho, R. Gray, Vector Quantization and Signal Compression (Kluwer Academic, Dordrecht, The Netherlands, 1992).
[CrossRef]

Haykin, S.

S. Haykin, Neural Networks (Macmillan, New York, 1994).

Haylo, N.

Huang, C.

V. Sitaram, C. Huang, P. Israelsen, “Efficient codebooks for vector quantization image compression with an adaptive tree search algorithm,” IEEE Trans. Commun. 42, 3027–3033 (1994).
[CrossRef]

C. Huang, Q. Bi, G. Stiles, “Fast full search equivalent encoding algorithms for image compression using vector quantization,” IEEE Trans. Image Process. 1, 413–416 (1992).
[CrossRef] [PubMed]

Huck, F.

Hurd, L.

M. Barnsley, L. Hurd, Fractal Image Compression (AK Peters, Wellesley, Mass., 1993).

Inoue, S.

S. Inoue, Video Microscopy (Plenum, New York, 1986).
[CrossRef]

Inouye, S.

S. Inouye, R. Oldenbourg, “Microscopes,” in Handbook of Optics, Vol. II (McGraw-Hill, New York, 1995), Chap. 17.

Israelsen, P.

V. Sitaram, C. Huang, P. Israelsen, “Efficient codebooks for vector quantization image compression with an adaptive tree search algorithm,” IEEE Trans. Commun. 42, 3027–3033 (1994).
[CrossRef]

Jones, P.

M. Rabbani, P. Jones, Digital Image Compression Techniques, Vol. TT07 of Tutorial Texts in Optical Engineering (Society of Photo-Optical Instrumentation Engineers, Bellingham, Wash., 1991).

Knowles, G.

A. Lewis, G. Knowles, “Image compression using 2-D wavelet transform,” IEEE Trans. Image Process. 1, 244–249 (1992).
[CrossRef]

Kohonen, T.

T. Kohonen, “The self-organizing map,” Proc. IEEE 78, 1464–1480 (1990).
[CrossRef]

Kondoh, H.

C. Saloma, C. Palmes-Saloma, H. Kondoh, “Site-specific confocal fluorescence imaging of biological microstructures in a turbid medium,” Phys. Med. Biol. 43, 1741–1759 (1998).
[CrossRef] [PubMed]

Lamba, C.

R. Zaciu, C. Lamba, G. Nicula, “Image compression using an overcomplete discrete wavelet transform,” IEEE Trans. Cons. Electron. 42, 800–807 (1996).
[CrossRef]

Le Gall, D.

D. Le Gall, “A video compression standard for multimedia applications,” Commun. ACM 32, 46–58 (1991).
[CrossRef]

Lewis, A.

A. Lewis, G. Knowles, “Image compression using 2-D wavelet transform,” IEEE Trans. Image Process. 1, 244–249 (1992).
[CrossRef]

Li, W.

W. Li, E. Salari, “A fast vector quantization encoding method for image compression,” IEEE Trans. Ciruits Syst. Video 5, 119–123 (1995).
[CrossRef]

Luttrell, S.

S. Luttrell, “Code vector density in topographic mappings,” IEEE Trans. Neural Netw. 2, 427–436 (1991).
[CrossRef]

Mandelbrot, B.

B. Mandelbrot, Fractal Geometry of Nature (Freeman, New York, 1983).

Manolakis, D.

J. Proakis, D. Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications, 2nd ed. (Macmillan, New York, 1992).

Martinetz, T.

H. Ritter, T. Martinetz, K. Schulter, Neural Computation and Self-Organizing Maps (Addison-Wesley, New York, 1992).

Nazario, M. A.

Nicula, G.

R. Zaciu, C. Lamba, G. Nicula, “Image compression using an overcomplete discrete wavelet transform,” IEEE Trans. Cons. Electron. 42, 800–807 (1996).
[CrossRef]

O’Neill, E.

E. O’Neill, Introduction to Statistical Optics (Addison-Wesley, New York, 1963).

Oldenbourg, R.

S. Inouye, R. Oldenbourg, “Microscopes,” in Handbook of Optics, Vol. II (McGraw-Hill, New York, 1995), Chap. 17.

Olshen, R.

P. Cosman, R. Gray, R. Olshen, “Evaluating quality of compressed medical images: SNR, subjective rating and diagnostic accuracy,” Proc. IEEE 82, 919–930 (1994).
[CrossRef]

Palmes-Saloma, C.

C. Saloma, C. Palmes-Saloma, H. Kondoh, “Site-specific confocal fluorescence imaging of biological microstructures in a turbid medium,” Phys. Med. Biol. 43, 1741–1759 (1998).
[CrossRef] [PubMed]

Proakis, J.

J. Proakis, D. Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications, 2nd ed. (Macmillan, New York, 1992).

Prost, R.

N. Akrout, R. Prost, R. Goutte, “Image compression by vector quantization: a review focused on codebook generation,” Image Vision Comput. 12, 627–638 (1994).
[CrossRef]

Prusinkiewicz, P.

S. Wan, S. Wong, P. Prusinkiewicz, “An algorithm for multidimensional data clustering,” ACM (Assoc. Comput. Mach.) Trans. Math. Software 14, 153–162 (1988).
[CrossRef]

Pugh, R.

R. Pugh, The Mouse: Its Reproduction and Development (Oxford University Press, New York, 1990).

Rabbani, M.

M. Rabbani, P. Jones, Digital Image Compression Techniques, Vol. TT07 of Tutorial Texts in Optical Engineering (Society of Photo-Optical Instrumentation Engineers, Bellingham, Wash., 1991).

Riskin, E.

M. Ting, E. Riskin, “Error-diffused image compression using a binary-to-gray-scale decoder and predictive pruned tree-structured vector quantization,” IEEE Trans. Image Process. 3, 854–857 (1994).
[CrossRef] [PubMed]

Ritter, H.

H. Ritter, T. Martinetz, K. Schulter, Neural Computation and Self-Organizing Maps (Addison-Wesley, New York, 1992).

Salari, E.

W. Li, E. Salari, “A fast vector quantization encoding method for image compression,” IEEE Trans. Ciruits Syst. Video 5, 119–123 (1995).
[CrossRef]

Saloma, C.

M. A. Nazario, C. Saloma, “Signal recovery in sinusoid-crossing sampling using the minimum-negativity constraint,” Appl. Opt. 37, 2953–2963 (1998).
[CrossRef]

C. Saloma, C. Palmes-Saloma, H. Kondoh, “Site-specific confocal fluorescence imaging of biological microstructures in a turbid medium,” Phys. Med. Biol. 43, 1741–1759 (1998).
[CrossRef] [PubMed]

M. Soriano, C. Saloma, “Improved classification robustness for noisy cell images represented as principal-component projections in a hybird recognition system,” Appl. Opt. 37, 3628–3639 (1998).
[CrossRef]

M. Soriano, C. Saloma, “Cell classification by a learning principal component analyzer and a backpropagation neural network,” Bioimaging 3, 168–175 (1995).
[CrossRef]

Samms, R.

Schulter, K.

H. Ritter, T. Martinetz, K. Schulter, Neural Computation and Self-Organizing Maps (Addison-Wesley, New York, 1992).

Sherlock, B.

B. Sherlock, M. Donald, “Fast discrete cosine transform,” ACM (Assoc. Comput. Mach.) Trans. Math. Software 21, 372–378 (1995).
[CrossRef]

Sitaram, V.

V. Sitaram, C. Huang, P. Israelsen, “Efficient codebooks for vector quantization image compression with an adaptive tree search algorithm,” IEEE Trans. Commun. 42, 3027–3033 (1994).
[CrossRef]

Soriano, M.

M. Soriano, C. Saloma, “Improved classification robustness for noisy cell images represented as principal-component projections in a hybird recognition system,” Appl. Opt. 37, 3628–3639 (1998).
[CrossRef]

M. Soriano, C. Saloma, “Cell classification by a learning principal component analyzer and a backpropagation neural network,” Bioimaging 3, 168–175 (1995).
[CrossRef]

Stacey, K.

Stiles, G.

C. Huang, Q. Bi, G. Stiles, “Fast full search equivalent encoding algorithms for image compression using vector quantization,” IEEE Trans. Image Process. 1, 413–416 (1992).
[CrossRef] [PubMed]

Ting, M.

M. Ting, E. Riskin, “Error-diffused image compression using a binary-to-gray-scale decoder and predictive pruned tree-structured vector quantization,” IEEE Trans. Image Process. 3, 854–857 (1994).
[CrossRef] [PubMed]

Tukey, J.

J. Cooley, J. Tukey, “An algorithm for the machine calculation of complex Fourier series,” Math. Comput. 19, 297–301 (1965).
[CrossRef]

Wallace, G.

G. Wallace, “The JPEG still picture compression standard,” Commun. ACM 32, 30–34 (1991).
[CrossRef]

Wan, S.

S. Wan, S. Wong, P. Prusinkiewicz, “An algorithm for multidimensional data clustering,” ACM (Assoc. Comput. Mach.) Trans. Math. Software 14, 153–162 (1988).
[CrossRef]

Wilson, T.

T. Wilson, “Confocal microscopy,” in Confocal Microscopy, T. Wilson, ed. (Academic, London, 1990), pp. 1–64.
[CrossRef]

Wong, S.

S. Wan, S. Wong, P. Prusinkiewicz, “An algorithm for multidimensional data clustering,” ACM (Assoc. Comput. Mach.) Trans. Math. Software 14, 153–162 (1988).
[CrossRef]

Woods, R.

R. Gonzales, R. Woods, Digital Image Processing (Addison-Wesley, New York, 1992).

Zaciu, R.

R. Zaciu, C. Lamba, G. Nicula, “Image compression using an overcomplete discrete wavelet transform,” IEEE Trans. Cons. Electron. 42, 800–807 (1996).
[CrossRef]

ACM (Assoc. Comput. Mach.) Trans. Math. Software (2)

B. Sherlock, M. Donald, “Fast discrete cosine transform,” ACM (Assoc. Comput. Mach.) Trans. Math. Software 21, 372–378 (1995).
[CrossRef]

S. Wan, S. Wong, P. Prusinkiewicz, “An algorithm for multidimensional data clustering,” ACM (Assoc. Comput. Mach.) Trans. Math. Software 14, 153–162 (1988).
[CrossRef]

Appl. Opt. (2)

Bioimaging (1)

M. Soriano, C. Saloma, “Cell classification by a learning principal component analyzer and a backpropagation neural network,” Bioimaging 3, 168–175 (1995).
[CrossRef]

Commun. ACM (2)

D. Le Gall, “A video compression standard for multimedia applications,” Commun. ACM 32, 46–58 (1991).
[CrossRef]

G. Wallace, “The JPEG still picture compression standard,” Commun. ACM 32, 30–34 (1991).
[CrossRef]

IEEE Trans. Ciruits Syst. Video (1)

W. Li, E. Salari, “A fast vector quantization encoding method for image compression,” IEEE Trans. Ciruits Syst. Video 5, 119–123 (1995).
[CrossRef]

IEEE Trans. Commun. (1)

V. Sitaram, C. Huang, P. Israelsen, “Efficient codebooks for vector quantization image compression with an adaptive tree search algorithm,” IEEE Trans. Commun. 42, 3027–3033 (1994).
[CrossRef]

IEEE Trans. Cons. Electron. (1)

R. Zaciu, C. Lamba, G. Nicula, “Image compression using an overcomplete discrete wavelet transform,” IEEE Trans. Cons. Electron. 42, 800–807 (1996).
[CrossRef]

IEEE Trans. Image Process. (3)

M. Ting, E. Riskin, “Error-diffused image compression using a binary-to-gray-scale decoder and predictive pruned tree-structured vector quantization,” IEEE Trans. Image Process. 3, 854–857 (1994).
[CrossRef] [PubMed]

C. Huang, Q. Bi, G. Stiles, “Fast full search equivalent encoding algorithms for image compression using vector quantization,” IEEE Trans. Image Process. 1, 413–416 (1992).
[CrossRef] [PubMed]

A. Lewis, G. Knowles, “Image compression using 2-D wavelet transform,” IEEE Trans. Image Process. 1, 244–249 (1992).
[CrossRef]

IEEE Trans. Neural Netw. (1)

S. Luttrell, “Code vector density in topographic mappings,” IEEE Trans. Neural Netw. 2, 427–436 (1991).
[CrossRef]

Image Vision Comput. (1)

N. Akrout, R. Prost, R. Goutte, “Image compression by vector quantization: a review focused on codebook generation,” Image Vision Comput. 12, 627–638 (1994).
[CrossRef]

J. Opt. Soc. Am. A (1)

Math. Comput. (1)

J. Cooley, J. Tukey, “An algorithm for the machine calculation of complex Fourier series,” Math. Comput. 19, 297–301 (1965).
[CrossRef]

Phys. Med. Biol. (1)

C. Saloma, C. Palmes-Saloma, H. Kondoh, “Site-specific confocal fluorescence imaging of biological microstructures in a turbid medium,” Phys. Med. Biol. 43, 1741–1759 (1998).
[CrossRef] [PubMed]

Physica D (1)

J. Bradley, C. Brislawn, “Image compression by vector quantization of multiresolution decomposition,” Physica D 60, 245–258 (1992).
[CrossRef]

Proc. IEEE (2)

T. Kohonen, “The self-organizing map,” Proc. IEEE 78, 1464–1480 (1990).
[CrossRef]

P. Cosman, R. Gray, R. Olshen, “Evaluating quality of compressed medical images: SNR, subjective rating and diagnostic accuracy,” Proc. IEEE 82, 919–930 (1994).
[CrossRef]

Other (15)

R. Pugh, The Mouse: Its Reproduction and Development (Oxford University Press, New York, 1990).

S. Inouye, R. Oldenbourg, “Microscopes,” in Handbook of Optics, Vol. II (McGraw-Hill, New York, 1995), Chap. 17.

E. O’Neill, Introduction to Statistical Optics (Addison-Wesley, New York, 1963).

J. Foley, Computer Graphics: Principles and Practice (Addison-Wesley, New York, 1990).

J. Proakis, D. Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications, 2nd ed. (Macmillan, New York, 1992).

M. Barnsley, L. Hurd, Fractal Image Compression (AK Peters, Wellesley, Mass., 1993).

B. Mandelbrot, Fractal Geometry of Nature (Freeman, New York, 1983).

J. Pawley, ed., Handbook of Biological Confocal Microscopy (Plenum, New York, 1995).

R. Gonzales, R. Woods, Digital Image Processing (Addison-Wesley, New York, 1992).

M. Rabbani, P. Jones, Digital Image Compression Techniques, Vol. TT07 of Tutorial Texts in Optical Engineering (Society of Photo-Optical Instrumentation Engineers, Bellingham, Wash., 1991).

A. Gersho, R. Gray, Vector Quantization and Signal Compression (Kluwer Academic, Dordrecht, The Netherlands, 1992).
[CrossRef]

S. Haykin, Neural Networks (Macmillan, New York, 1994).

H. Ritter, T. Martinetz, K. Schulter, Neural Computation and Self-Organizing Maps (Addison-Wesley, New York, 1992).

S. Inoue, Video Microscopy (Plenum, New York, 1986).
[CrossRef]

T. Wilson, “Confocal microscopy,” in Confocal Microscopy, T. Wilson, ed. (Academic, London, 1990), pp. 1–64.
[CrossRef]

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Figures (9)

Fig. 1
Fig. 1

Comparison of VQ compression with JPEG compression: (a) macroscopic objects (R = 1/12), (b) mouse embryo (R = 1/36). Arrangement of images: original image (leftmost), decompressed VQ image (middle), and decompressed JPEG image (rightmost).

Fig. 2
Fig. 2

Comparison of embryo image corrupted with additive Gaussian noise with standard deviations of (a) 101.12, (b) 68.9. Arrangement of images: original image (leftmost), decompressed VQ image (middle), and decompressed JPEG image (rightmost).

Fig. 3
Fig. 3

Video images of a polystyrene microsphere that is moved from left to right by a focused laser light: (a) three of five original image frames, (b) reconstructions with a codebook that is derived from image 1 (R = 1/15). Diameter of spheres, 7 µm.

Fig. 4
Fig. 4

Video microscopy. Plots of (a) NMSE, (b) Q, (c) S, and (d) F for the five reconstructions as a function of the image used to generate the codebook (R = 1/15; bs = 2 × 2 pixels; cb = 256; dr = 2/1 horizontal, 2/1 vertical).

Fig. 5
Fig. 5

Confocal fluorescence microscopy: (a) original, (b) reconstructions with image 1 as the codebook generator (R = 1/13). The images pertain to the hindbrain of a 10.5-dpc mouse embryo that was stained to fluoresce at 515 nm.

Fig. 6
Fig. 6

Confocal fluorescence microscopy. Plots of image quality as a function of the codebook generator (R = 1/6): (a) NMSE, (b) Q, (c) F, (d) S.

Fig. 7
Fig. 7

Confocal fluorescence microscopy. NMSE plots of image 1 reconstruction for different compression ratios R and codebook generators.

Fig. 8
Fig. 8

Images of embryo eye (age, 11.5 dpc) taken at different magnifications: (a) original, (b) reconstructions. Magnifications used: image 1 (5×), image 2 (10×), image 3 (20×, image 4 (40×), image 5 (62×).

Fig. 9
Fig. 9

Images of different magnifications. Plots of image quality as a function of the codebook generator (R = 1/13): (a) NMSE, (b) Q, (c) F, (d) S.

Equations (5)

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TCw= K-CwPKdK,
Cg=1/NK1+K2++KD,
ECw=n=1N Kn-Cw2.
Cw=1/NK1+K2++KN.
9/163/163/161/16.

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