A new postprocessing algorithm based on regression functions

In this paper, we propose a new postprocessing algorithm that reduces the blocking artifacts in low-rate coded images. It consists of two-step operations: low-pass £ltering and image estimation. The latter makes an estimation of the original image from the £ltered image based on regression functions. Regression functions for the JPEG-coded real images are numerically evaluated from a training set, and their piecewise linear approximation are used for the estimation. This approximate unbiased estimator is applied adaptively, depending on the DCT coef£cients. This proposed approach is a generalization of the existing methods as QCS [6] and NQCS [3], [4] can be regarded as the same type that employ biased estimators. Simulation results show that the new algorithm outperforms the existing methods in both objective and subjective qualities.