Yohei Saika and Yuji Haraguchi
Maximizer of the Posterior Marginal Estimate for Noise Reduction of JPEGcompressed Image
296 - 300
2012
6
3
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/11109
https://publications.waset.org/vol/63
World Academy of Science, Engineering and Technology
We constructed a method of noise reduction for
JPEGcompressed 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 JPEGcompressed
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 256grayscale
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.
International Science Index 63, 2012