Maximizer of the Posterior Marginal Estimate for Noise Reduction of JPEG-compressed Image
We constructed a method of noise reduction for
JPEG-compressed image based on Bayesian inference using the
maximizer of the posterior marginal (MPM) estimate. In this method,
we tried the MPM estimate using two kinds of likelihood, both of
which enhance grayscale images converted into the JPEG-compressed
image through the lossy JPEG image compression. One is the
deterministic model of the likelihood and the other is the probabilistic
one expressed by the Gaussian distribution. Then, using the Monte
Carlo simulation for grayscale images, such as the 256-grayscale
standard image “Lena" with 256 × 256 pixels, we examined the
performance of the MPM estimate based on the performance measure
using the mean square error. We clarified that the MPM estimate via
the Gaussian probabilistic model of the likelihood is effective for
reducing noises, such as the blocking artifacts and the mosquito noise,
if we set parameters appropriately. On the other hand, we found that
the MPM estimate via the deterministic model of the likelihood is not
effective for noise reduction due to the low acceptance ratio of the
Metropolis algorithm.
[1] W. B. Pennebaker and J. L. Mitchell, "JPEG Still Image Compression
Standard", Springer, New York, Van Nostrand Reinhold, 1992.
[2] H. C. Reeves and J. S. Lim, "Reduction of Blocking Effects in image
coding", Opt. Eng., Vol. 23, pp. 34-37, June, 1984.
[3] G. Ramamurthi and A. Gersho, "Nonlinear space-variant postprocessing
of block coded im ages", IEEE Trans. Acoust. Speech, Signal Processing,
Vol. ASSP-34, pp. 1258-1269, Oct, 1986.
[4] R. Rosenholdts and A. Zakhor, "Iterative procedures for reduction of
blocking effects in transform image coding", IEEE Trans. Circuits Syst.
Video Technol., Vol. 2, pp. 91-95, Mar, 1991.
[5] Y. Yang, N. P. Galastsanos, and A. K. Katsaggelos, "Regularized
reconstruction to reduce blocking artifacts of block discrete cosine
transform compressed images", IEEE Trans. Circuits Syst. Video
Technol., Vol. 3, pp. 421-432, Dec., 1993.
[6] J. Mateos, A, K. Katsaggelos, "A Bayesian Approach for the Estimation
and Transmission of Regularization Paramters for Reducing Blocing
Artifacts", IEEE Trans. Image Processing, vol. 9, pp. 1200-1215, July,
2000.
[7] T. Ozcelik, J. C. Brailean, and A. K. Katsaggelos, "Image and Video
Compression Algorithm Based on Recovery Techniques Using Mean
Field Annealing", Proc. Of the IEEE. Vol. 83, Sep, 1995.
[8] J. Marroquin, S. Mitter and T. Possio, "", the Journal of the American
Statistical Association, vol. 82, pp. 76-89, 1987.
[9] J. M. Pryce and A. D. Bruce, "Statistical mechanics of image restoration",
Journal of Physics A, vol. 28, pp. 511-532, Feb., 1995.
[10] H. Nishimori, "Statistical Physics of Spin Glasses and Information
Processing; An Introduction", Oxford, Oxford Press, July, 2001.
[11] H. Nishimori and K. Y. M. Wong, "Statistical mechanics of image
restoration and error-correcting codes", Physical Review E, Vol. 60, pp.
132-144, Jan, 1999.
[12] K. Tanaka. "Statistical-mechanical approach to image processing
(Topical Review)", J. Phys. A Mathematical and General, Vol. 35, Sep,
R31-R150, 2002.
[13] T. Murayama, Y. Kabashima, D. Saad and R. Vicente, "Statistical
physics of regular low-density parity-checking error-correcting codes",
Phys. Rev. E, Vol. 62, pp.1577-1591, Aug., 2000.
[14] A. Kanemura, S. Maeda and S. Ishii, "Superresolution with compound
Markov random fields via the variational EM algorithm. Neural Networks,
Vol. 22, pp. 1025-1034, July, 2009.
[15] S. Morita and H. Nishimori, "Convergence of quantum annealing with
real time Schrodinger dynamics", J. Phys. Soc. Jpn., Vol. 76,
064002-064004, May, 2007.
[1] W. B. Pennebaker and J. L. Mitchell, "JPEG Still Image Compression
Standard", Springer, New York, Van Nostrand Reinhold, 1992.
[2] H. C. Reeves and J. S. Lim, "Reduction of Blocking Effects in image
coding", Opt. Eng., Vol. 23, pp. 34-37, June, 1984.
[3] G. Ramamurthi and A. Gersho, "Nonlinear space-variant postprocessing
of block coded im ages", IEEE Trans. Acoust. Speech, Signal Processing,
Vol. ASSP-34, pp. 1258-1269, Oct, 1986.
[4] R. Rosenholdts and A. Zakhor, "Iterative procedures for reduction of
blocking effects in transform image coding", IEEE Trans. Circuits Syst.
Video Technol., Vol. 2, pp. 91-95, Mar, 1991.
[5] Y. Yang, N. P. Galastsanos, and A. K. Katsaggelos, "Regularized
reconstruction to reduce blocking artifacts of block discrete cosine
transform compressed images", IEEE Trans. Circuits Syst. Video
Technol., Vol. 3, pp. 421-432, Dec., 1993.
[6] J. Mateos, A, K. Katsaggelos, "A Bayesian Approach for the Estimation
and Transmission of Regularization Paramters for Reducing Blocing
Artifacts", IEEE Trans. Image Processing, vol. 9, pp. 1200-1215, July,
2000.
[7] T. Ozcelik, J. C. Brailean, and A. K. Katsaggelos, "Image and Video
Compression Algorithm Based on Recovery Techniques Using Mean
Field Annealing", Proc. Of the IEEE. Vol. 83, Sep, 1995.
[8] J. Marroquin, S. Mitter and T. Possio, "", the Journal of the American
Statistical Association, vol. 82, pp. 76-89, 1987.
[9] J. M. Pryce and A. D. Bruce, "Statistical mechanics of image restoration",
Journal of Physics A, vol. 28, pp. 511-532, Feb., 1995.
[10] H. Nishimori, "Statistical Physics of Spin Glasses and Information
Processing; An Introduction", Oxford, Oxford Press, July, 2001.
[11] H. Nishimori and K. Y. M. Wong, "Statistical mechanics of image
restoration and error-correcting codes", Physical Review E, Vol. 60, pp.
132-144, Jan, 1999.
[12] K. Tanaka. "Statistical-mechanical approach to image processing
(Topical Review)", J. Phys. A Mathematical and General, Vol. 35, Sep,
R31-R150, 2002.
[13] T. Murayama, Y. Kabashima, D. Saad and R. Vicente, "Statistical
physics of regular low-density parity-checking error-correcting codes",
Phys. Rev. E, Vol. 62, pp.1577-1591, Aug., 2000.
[14] A. Kanemura, S. Maeda and S. Ishii, "Superresolution with compound
Markov random fields via the variational EM algorithm. Neural Networks,
Vol. 22, pp. 1025-1034, July, 2009.
[15] S. Morita and H. Nishimori, "Convergence of quantum annealing with
real time Schrodinger dynamics", J. Phys. Soc. Jpn., Vol. 76,
064002-064004, May, 2007.
@article{"International Journal of Information, Control and Computer Sciences:60157", author = "Yohei Saika and Yuji Haraguchi", title = "Maximizer of the Posterior Marginal Estimate for Noise Reduction of JPEG-compressed Image", abstract = "We constructed a method of noise reduction for
JPEG-compressed image based on Bayesian inference using the
maximizer of the posterior marginal (MPM) estimate. In this method,
we tried the MPM estimate using two kinds of likelihood, both of
which enhance grayscale images converted into the JPEG-compressed
image through the lossy JPEG image compression. One is the
deterministic model of the likelihood and the other is the probabilistic
one expressed by the Gaussian distribution. Then, using the Monte
Carlo simulation for grayscale images, such as the 256-grayscale
standard image “Lena" with 256 × 256 pixels, we examined the
performance of the MPM estimate based on the performance measure
using the mean square error. We clarified that the MPM estimate via
the Gaussian probabilistic model of the likelihood is effective for
reducing noises, such as the blocking artifacts and the mosquito noise,
if we set parameters appropriately. On the other hand, we found that
the MPM estimate via the deterministic model of the likelihood is not
effective for noise reduction due to the low acceptance ratio of the
Metropolis algorithm.", keywords = "Noise reduction, JPEG-compressed image, Bayesian
inference, the maximizer of the posterior marginal estimate", volume = "6", number = "3", pages = "387-5", }