Abstract

The performance of an image compression scheme is affected by the presence of noise, and the achievable compression may be reduced significantly. We investigated the effects of specific signal-dependent-noise (SDN) sources, such as film-grain and speckle noise, on image compression, using JPEG (Joint Photographic Experts Group) standard image compression. For the improvement of compression ratios noisy images are preprocessed for noise suppression before compression is applied. Two approaches are employed for noise suppression. In one approach an estimator designed specifically for the SDN model is used. In an alternate approach, the noise is first transformed into signal-independent noise (SIN) and then an estimator designed for SIN is employed. The performances of these two schemes are compared. The compression results achieved for noiseless, noisy, and restored images are also presented.

© 1999 Optical Society of America

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References

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  1. J. Vaaben, B. Niss, “Compressing images with JPEG,” Inf. Disp. 7, 12–14 (1991).
  2. H. H. Arsenault, C. Gendron, M. Denis, “Transformation of film-grain noise into signal-independent additive Gaussian noise,” J. Opt. Soc. Am. 71, 91–94 (1981).
    [CrossRef]
  3. D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust. Speech Signal Process. ASSP-35, 373–382 (1987).
    [CrossRef]
  4. S. Mitra, R. A. Muyshondt, S. Pemmaraju, “Hybrid high-fidelity image compression technique using multi-scale wavelets,” in Wavelet Applications in Signal and Image Processing III, A. F. Laine, M. A. Unser, M. V. Wickerhauser, eds., Proc. SPIE2569, 623–630 (1995).
    [CrossRef]
  5. C. Y. Chang, R. Kwok, J. C. Curlander, “Spatial compression of seasat SAR imagery,” IEEE Trans. Geosci. Remote Sens. 26, 673–685 (1988).
    [CrossRef]
  6. C. Kappeler, S. P. Muller, “Wavelet compression for noisy tomographic images,” in Wavelet Applications in Signal and Image Processing III, A. F. Laine, M. A. Unser, M. V. Wickerhauser, eds., Proc. SPIE2569, 644–652 (1995).
    [CrossRef]
  7. O. K. Al-Shaykh, R. M. Mersereau, “Lossy compression of noisy cardiac image sequences,” in Proceedings of the 1996 Data Compression Conference (IEEE Computer Society Press, Los Alamitos, Calif., 1996), pp. 43–52.
  8. O. K. Al-Shaykh, R. M. Mersereau, “Lossy compression of images corrupted by film grain noise,” in Proceedings of the 3rd IEEE International Conference on Image Processing (IEEE Signal Processing Society, N.Y., 1996), Vol. 1, pp. 805–808.
    [CrossRef]
  9. J. Zhang, “The mean field theory in EM procedures for blind Markov random field image restoration,” IEEE Trans. Image Process. 2, 27–40 (1993).
    [CrossRef] [PubMed]
  10. J. K. Wolf, J. Ziv, “Transmission of noisy information to a noisy receiver with minimum distortion,” IEEE Trans. Inf. Theory IT-16, 406–411 (1970).
    [CrossRef]
  11. Y. Ephraim, R. M. Gray, “A unified approach for encoding clean and noisy sources by means of waveform and autoregressive model vector quantization,” IEEE Trans. Inf. Theory 34, 826–834 (1988).
    [CrossRef]
  12. D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7, 165–177 (1985).
    [CrossRef]
  13. H. H. Arsenault, M. Denis, “Integral expression for transforming signal-dependent noise into signal-independent noise,” Opt. Lett. 6, 210–212 (1981).
    [CrossRef] [PubMed]
  14. P. R. Prucnal, B. E. A. Saleh, “Transformation of image-signal-dependent noise into image-signal-independent noise,” Opt. Lett. 6, 316–318 (1981).
    [CrossRef] [PubMed]
  15. W. B. Pennebaker, J. L. Mitchel, JPEG Still Image Data Compression Standard (Van Nostrand, New York, 1993).
  16. G. K. Froehlich, J. F. Walkup, R. B. Asher, “Optimal estimation in signal-dependent noise,” J. Opt. Soc. Am. 68, 1665–1672 (1978).
    [CrossRef]
  17. H. H. Arsenault, M. Levesque, “Combined homomorphic and local-statistics processing for restoration of images degraded by signal-dependent noise,” Appl. Opt. 23, 845–850 (1984).
    [CrossRef] [PubMed]
  18. J. S. Lim, H. Nawab, “Techniques for speckle noise removal,” Opt. Eng. 20, 472–480 (1981).
    [CrossRef]
  19. D. T. Kuan, Nonstationary Recursive Restoration of Images with Signal-Dependent Noise with Application to Speckle Reduction, (Department of Electrical Engineering, University of Southern California, Los Angeles, Calif., 1982).
  20. Independent JPEG Group’s documentation of JPEG software for release 5September1994 ( http://www.ijg.org ).
  21. P. W. Melnychuck, M. J. Barry, M. S. Mathieu, “The effect of noise and MTF on the compressibility of high resolution color images,” in Image-Processing Algorithms and Techniques, R. J. Moorehead, K. S. Pennington, eds., Proc. SPIE1244, 255–262 (1990).
    [CrossRef]
  22. G. K. Froehlich, J. F. Walkup, T. F. Krile, “Estimation in signal-dependent film-grain noise,” Appl. Opt. 20, 3619–3626 (1981).
    [CrossRef] [PubMed]
  23. R. Shahnaz, “Image compression in signal-dependent noise,” Master of Science thesis (Texas Tech University, Lubbock, Texas, 1995).

1993 (1)

J. Zhang, “The mean field theory in EM procedures for blind Markov random field image restoration,” IEEE Trans. Image Process. 2, 27–40 (1993).
[CrossRef] [PubMed]

1991 (1)

J. Vaaben, B. Niss, “Compressing images with JPEG,” Inf. Disp. 7, 12–14 (1991).

1988 (2)

C. Y. Chang, R. Kwok, J. C. Curlander, “Spatial compression of seasat SAR imagery,” IEEE Trans. Geosci. Remote Sens. 26, 673–685 (1988).
[CrossRef]

Y. Ephraim, R. M. Gray, “A unified approach for encoding clean and noisy sources by means of waveform and autoregressive model vector quantization,” IEEE Trans. Inf. Theory 34, 826–834 (1988).
[CrossRef]

1987 (1)

D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust. Speech Signal Process. ASSP-35, 373–382 (1987).
[CrossRef]

1985 (1)

D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7, 165–177 (1985).
[CrossRef]

1984 (1)

1981 (5)

1978 (1)

1970 (1)

J. K. Wolf, J. Ziv, “Transmission of noisy information to a noisy receiver with minimum distortion,” IEEE Trans. Inf. Theory IT-16, 406–411 (1970).
[CrossRef]

Al-Shaykh, O. K.

O. K. Al-Shaykh, R. M. Mersereau, “Lossy compression of noisy cardiac image sequences,” in Proceedings of the 1996 Data Compression Conference (IEEE Computer Society Press, Los Alamitos, Calif., 1996), pp. 43–52.

O. K. Al-Shaykh, R. M. Mersereau, “Lossy compression of images corrupted by film grain noise,” in Proceedings of the 3rd IEEE International Conference on Image Processing (IEEE Signal Processing Society, N.Y., 1996), Vol. 1, pp. 805–808.
[CrossRef]

Arsenault, H. H.

Asher, R. B.

Barry, M. J.

P. W. Melnychuck, M. J. Barry, M. S. Mathieu, “The effect of noise and MTF on the compressibility of high resolution color images,” in Image-Processing Algorithms and Techniques, R. J. Moorehead, K. S. Pennington, eds., Proc. SPIE1244, 255–262 (1990).
[CrossRef]

Chang, C. Y.

C. Y. Chang, R. Kwok, J. C. Curlander, “Spatial compression of seasat SAR imagery,” IEEE Trans. Geosci. Remote Sens. 26, 673–685 (1988).
[CrossRef]

Chavel, P.

D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust. Speech Signal Process. ASSP-35, 373–382 (1987).
[CrossRef]

D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7, 165–177 (1985).
[CrossRef]

Curlander, J. C.

C. Y. Chang, R. Kwok, J. C. Curlander, “Spatial compression of seasat SAR imagery,” IEEE Trans. Geosci. Remote Sens. 26, 673–685 (1988).
[CrossRef]

Denis, M.

Ephraim, Y.

Y. Ephraim, R. M. Gray, “A unified approach for encoding clean and noisy sources by means of waveform and autoregressive model vector quantization,” IEEE Trans. Inf. Theory 34, 826–834 (1988).
[CrossRef]

Froehlich, G. K.

Gendron, C.

Gray, R. M.

Y. Ephraim, R. M. Gray, “A unified approach for encoding clean and noisy sources by means of waveform and autoregressive model vector quantization,” IEEE Trans. Inf. Theory 34, 826–834 (1988).
[CrossRef]

Kappeler, C.

C. Kappeler, S. P. Muller, “Wavelet compression for noisy tomographic images,” in Wavelet Applications in Signal and Image Processing III, A. F. Laine, M. A. Unser, M. V. Wickerhauser, eds., Proc. SPIE2569, 644–652 (1995).
[CrossRef]

Krile, T. F.

Kuan, D. T.

D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust. Speech Signal Process. ASSP-35, 373–382 (1987).
[CrossRef]

D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7, 165–177 (1985).
[CrossRef]

D. T. Kuan, Nonstationary Recursive Restoration of Images with Signal-Dependent Noise with Application to Speckle Reduction, (Department of Electrical Engineering, University of Southern California, Los Angeles, Calif., 1982).

Kwok, R.

C. Y. Chang, R. Kwok, J. C. Curlander, “Spatial compression of seasat SAR imagery,” IEEE Trans. Geosci. Remote Sens. 26, 673–685 (1988).
[CrossRef]

Levesque, M.

Lim, J. S.

J. S. Lim, H. Nawab, “Techniques for speckle noise removal,” Opt. Eng. 20, 472–480 (1981).
[CrossRef]

Mathieu, M. S.

P. W. Melnychuck, M. J. Barry, M. S. Mathieu, “The effect of noise and MTF on the compressibility of high resolution color images,” in Image-Processing Algorithms and Techniques, R. J. Moorehead, K. S. Pennington, eds., Proc. SPIE1244, 255–262 (1990).
[CrossRef]

Melnychuck, P. W.

P. W. Melnychuck, M. J. Barry, M. S. Mathieu, “The effect of noise and MTF on the compressibility of high resolution color images,” in Image-Processing Algorithms and Techniques, R. J. Moorehead, K. S. Pennington, eds., Proc. SPIE1244, 255–262 (1990).
[CrossRef]

Mersereau, R. M.

O. K. Al-Shaykh, R. M. Mersereau, “Lossy compression of images corrupted by film grain noise,” in Proceedings of the 3rd IEEE International Conference on Image Processing (IEEE Signal Processing Society, N.Y., 1996), Vol. 1, pp. 805–808.
[CrossRef]

O. K. Al-Shaykh, R. M. Mersereau, “Lossy compression of noisy cardiac image sequences,” in Proceedings of the 1996 Data Compression Conference (IEEE Computer Society Press, Los Alamitos, Calif., 1996), pp. 43–52.

Mitchel, J. L.

W. B. Pennebaker, J. L. Mitchel, JPEG Still Image Data Compression Standard (Van Nostrand, New York, 1993).

Mitra, S.

S. Mitra, R. A. Muyshondt, S. Pemmaraju, “Hybrid high-fidelity image compression technique using multi-scale wavelets,” in Wavelet Applications in Signal and Image Processing III, A. F. Laine, M. A. Unser, M. V. Wickerhauser, eds., Proc. SPIE2569, 623–630 (1995).
[CrossRef]

Muller, S. P.

C. Kappeler, S. P. Muller, “Wavelet compression for noisy tomographic images,” in Wavelet Applications in Signal and Image Processing III, A. F. Laine, M. A. Unser, M. V. Wickerhauser, eds., Proc. SPIE2569, 644–652 (1995).
[CrossRef]

Muyshondt, R. A.

S. Mitra, R. A. Muyshondt, S. Pemmaraju, “Hybrid high-fidelity image compression technique using multi-scale wavelets,” in Wavelet Applications in Signal and Image Processing III, A. F. Laine, M. A. Unser, M. V. Wickerhauser, eds., Proc. SPIE2569, 623–630 (1995).
[CrossRef]

Nawab, H.

J. S. Lim, H. Nawab, “Techniques for speckle noise removal,” Opt. Eng. 20, 472–480 (1981).
[CrossRef]

Niss, B.

J. Vaaben, B. Niss, “Compressing images with JPEG,” Inf. Disp. 7, 12–14 (1991).

Pemmaraju, S.

S. Mitra, R. A. Muyshondt, S. Pemmaraju, “Hybrid high-fidelity image compression technique using multi-scale wavelets,” in Wavelet Applications in Signal and Image Processing III, A. F. Laine, M. A. Unser, M. V. Wickerhauser, eds., Proc. SPIE2569, 623–630 (1995).
[CrossRef]

Pennebaker, W. B.

W. B. Pennebaker, J. L. Mitchel, JPEG Still Image Data Compression Standard (Van Nostrand, New York, 1993).

Prucnal, P. R.

Saleh, B. E. A.

Sawchuk, A. A.

D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust. Speech Signal Process. ASSP-35, 373–382 (1987).
[CrossRef]

D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7, 165–177 (1985).
[CrossRef]

Shahnaz, R.

R. Shahnaz, “Image compression in signal-dependent noise,” Master of Science thesis (Texas Tech University, Lubbock, Texas, 1995).

Strand, T. C.

D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust. Speech Signal Process. ASSP-35, 373–382 (1987).
[CrossRef]

D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7, 165–177 (1985).
[CrossRef]

Vaaben, J.

J. Vaaben, B. Niss, “Compressing images with JPEG,” Inf. Disp. 7, 12–14 (1991).

Walkup, J. F.

Wolf, J. K.

J. K. Wolf, J. Ziv, “Transmission of noisy information to a noisy receiver with minimum distortion,” IEEE Trans. Inf. Theory IT-16, 406–411 (1970).
[CrossRef]

Zhang, J.

J. Zhang, “The mean field theory in EM procedures for blind Markov random field image restoration,” IEEE Trans. Image Process. 2, 27–40 (1993).
[CrossRef] [PubMed]

Ziv, J.

J. K. Wolf, J. Ziv, “Transmission of noisy information to a noisy receiver with minimum distortion,” IEEE Trans. Inf. Theory IT-16, 406–411 (1970).
[CrossRef]

Appl. Opt. (2)

IEEE Trans. Acoust. Speech Signal Process. (1)

D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust. Speech Signal Process. ASSP-35, 373–382 (1987).
[CrossRef]

IEEE Trans. Geosci. Remote Sens. (1)

C. Y. Chang, R. Kwok, J. C. Curlander, “Spatial compression of seasat SAR imagery,” IEEE Trans. Geosci. Remote Sens. 26, 673–685 (1988).
[CrossRef]

IEEE Trans. Image Process. (1)

J. Zhang, “The mean field theory in EM procedures for blind Markov random field image restoration,” IEEE Trans. Image Process. 2, 27–40 (1993).
[CrossRef] [PubMed]

IEEE Trans. Inf. Theory (2)

J. K. Wolf, J. Ziv, “Transmission of noisy information to a noisy receiver with minimum distortion,” IEEE Trans. Inf. Theory IT-16, 406–411 (1970).
[CrossRef]

Y. Ephraim, R. M. Gray, “A unified approach for encoding clean and noisy sources by means of waveform and autoregressive model vector quantization,” IEEE Trans. Inf. Theory 34, 826–834 (1988).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7, 165–177 (1985).
[CrossRef]

Inf. Disp. (1)

J. Vaaben, B. Niss, “Compressing images with JPEG,” Inf. Disp. 7, 12–14 (1991).

J. Opt. Soc. Am. (2)

Opt. Eng. (1)

J. S. Lim, H. Nawab, “Techniques for speckle noise removal,” Opt. Eng. 20, 472–480 (1981).
[CrossRef]

Opt. Lett. (2)

Other (9)

W. B. Pennebaker, J. L. Mitchel, JPEG Still Image Data Compression Standard (Van Nostrand, New York, 1993).

D. T. Kuan, Nonstationary Recursive Restoration of Images with Signal-Dependent Noise with Application to Speckle Reduction, (Department of Electrical Engineering, University of Southern California, Los Angeles, Calif., 1982).

Independent JPEG Group’s documentation of JPEG software for release 5September1994 ( http://www.ijg.org ).

P. W. Melnychuck, M. J. Barry, M. S. Mathieu, “The effect of noise and MTF on the compressibility of high resolution color images,” in Image-Processing Algorithms and Techniques, R. J. Moorehead, K. S. Pennington, eds., Proc. SPIE1244, 255–262 (1990).
[CrossRef]

S. Mitra, R. A. Muyshondt, S. Pemmaraju, “Hybrid high-fidelity image compression technique using multi-scale wavelets,” in Wavelet Applications in Signal and Image Processing III, A. F. Laine, M. A. Unser, M. V. Wickerhauser, eds., Proc. SPIE2569, 623–630 (1995).
[CrossRef]

C. Kappeler, S. P. Muller, “Wavelet compression for noisy tomographic images,” in Wavelet Applications in Signal and Image Processing III, A. F. Laine, M. A. Unser, M. V. Wickerhauser, eds., Proc. SPIE2569, 644–652 (1995).
[CrossRef]

O. K. Al-Shaykh, R. M. Mersereau, “Lossy compression of noisy cardiac image sequences,” in Proceedings of the 1996 Data Compression Conference (IEEE Computer Society Press, Los Alamitos, Calif., 1996), pp. 43–52.

O. K. Al-Shaykh, R. M. Mersereau, “Lossy compression of images corrupted by film grain noise,” in Proceedings of the 3rd IEEE International Conference on Image Processing (IEEE Signal Processing Society, N.Y., 1996), Vol. 1, pp. 805–808.
[CrossRef]

R. Shahnaz, “Image compression in signal-dependent noise,” Master of Science thesis (Texas Tech University, Lubbock, Texas, 1995).

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Figures (8)

Fig. 1
Fig. 1

Lena image (noiseless).

Fig. 2
Fig. 2

Two alternative noise suppression schemes for SDN.

Fig. 3
Fig. 3

Noisy Lena with FGN (C = 2) and the restored image obtained with the adaptive noise smoothing filter (5 × 5). (a) Lena with FGN (C = 2), MSE = 502; (b) restored image, MSE = 86.

Fig. 4
Fig. 4

Image restored from the noisy Lena image with FGN (C = 2) with HT and J–S estimator, MSE = 92.

Fig. 5
Fig. 5

CR versus Q plot for noiseless, noisy (FGN, C = 2), and restored (by use of adaptive noise smoothing filter) Lena images.

Fig. 6
Fig. 6

Four-frames-averaged speckle image and restored image with adaptive noise smoothing filter (5 × 5). (a) Lena image with speckle noise (four frames averaged), MSE = 2424; (b) restored Lena image, MSE = 960.

Fig. 7
Fig. 7

Images restored from four-frames-averaged speckle image with HT and J–S estimator. (a) Restored Lena image with a 5 × 5 window and single iteration, MSE = 682; (b) restored Lena image with a 3 × 3 window and double iteration, MSE = 349.

Fig. 8
Fig. 8

CR versus Q plot for images restored from the speckle-degraded Lena image.

Tables (2)

Tables Icon

Table 1 Compression Results for Different Quality Factors for Noiseless, Noisy,a and Restoredb Lena Images

Tables Icon

Table 2 Compression Results for Image with Speckle Noise and Images Restored from Speckle Image

Equations (31)

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

rk, l=fk, l+Cfk, l1/2nk, l,
rk, l=fk, lnk, l,
pnk, l=exp-nk, lfor nk, l00otherwise.
rak, l=1Mi=1M rik, l,
SF=50Qfor 1Q50200-2Q100for 50<Q<100.
MSE=1N2i=1Nj=1Nfi, j-fˆi, j2,
r_=f_+u_,
fˆk, l=mfk, l+Vfk, lVfk, l+Vuk, lrk, l-mrk, l,
 mrk, l=12M+12N+1i=k-Mk+Mj=l-Nl+N ri, j,
Vrk, l=12M+12N+1-1i=k-Mk+Mj=l-Nl+Nri, j-mrk, l2,
fˆk, l=mrk, l+Vrk, l-σn2k, lVrk, l×rk, l-mrk, l,
fˆk, l=mrk, l+Wrk, l-mrk, l,
W=Max1-σn2Vr, 0.
uk, l=Cfk, l1/2nk, l.
Vfk, l=Vrk, l-C2mfk, lσn2k, l.
fˆk, l=mrk, l+Vrk, l-C2mfk, lσn2k, lVrk, l-Vuk, l+Vuk, l×rk, l-mrk, l
fˆk, l=mrk, l+WFrk, l-mrk, l,
WF=Max1-C2mrk, lσn2k, lVrk, l, 0.
fˆk, l=mrk, l+Vfk, lVfk, l+σn2mr2k, l+Vfk, l×rk, l-mrk, l,
Vfk, l=Vrk, l-σn2mr2k, l1+σn2,
fˆk, l=mrk, l+Wsrk, l-mrk, l,
WS=Max1-mr2k, lσn2Vrk, l1+σn2, 0.
σn2=1M.
rk, l=255rk, l1/2.
rk, l=rk, l2255.
σ=C2552.
rk, l=255ln256 lnrk, l+1.
rk, l=exprk, lln256255-1.
fˆk, l=mrk, l-msp+Wrk, l-mrk, l,
W=Max1-σsp2Vrk, l, 0
PSNR=10 log1025521N2i=1Nj=1Nxi, j-xci, j2dB,

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