Image Deblocking in Wavelet Domain Based on Local Laplace Prior

This paper presents a efficient non iterative wavelet based image deblocking method which employs a maximum a posteriori (MAP) estimator that uses the Laplace probability density function with local variance to smooth out the blocking artifacts in block discrete cosine transform (DCT) compressed images. We have shown that the noise standard deviation has a strong relationship with the average of first 3x3 values from the quantization table. The method is adaptive to different block DCT compressed images that are compressed at various bit rates. The proposed method outperforms several well known wavelet and non wavelet based methods both in terms of visual quality and peak signal to noise ratio (PSNR).