Blocking-artifact reduction in block-coded images using wavelet-based subband decomposition

We propose a post-processing method in the wavelet transform domain that can significantly reduce the blocking effects in low-bit-rate block-transform-coded images. Although the quantization noise of the transform coefficients is the sole source of error in a coded image, the properties of block transform make the errors appear in two categories: blocky noise, which causes blocking effects, and granular (nonblocky) noise. Noting that subband coding does not suffer from blocky noise, the proposed technique is designed to work in the subband domain. Once a coded image is decomposed into subbands by wavelet filters, most energy of the blocky noise exists on the predetermined block boundaries of their corresponding subbands. We can reduce the blocky noise by a linear minimum mean square error filter, which fully exploits the characteristics of the signal and noise components in each subband. After the blocky noise is reduced, the granular noise can further be decreased by exploiting its nonstructuredness. Computer simulations show that the proposed method visibly reduces the blocking effects in reconstructed images and yields better PSNR improvement.