TY - JFULL AU - Yohei Saika and Yuji Haraguchi PY - 2012/4/ TI - Maximizer of the Posterior Marginal Estimate for Noise Reduction of JPEG-compressed Image T2 - International Journal of Computer and Information Engineering SP - 295 EP - 300 VL - 6 SN - 1307-6892 UR - https://publications.waset.org/pdf/11109 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 63, 2012 N2 - 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. ER -