A Smoothness Constraint Set based on Local Statistics of BDCT Coefficients for Image Postprocessing

Abstract

In blocking artifacts reduction based on the projection onto convex sets (POCS) technique, good constraint sets are very important. Until recently, smoothness constraint sets (SCS) are often formulated in the image domain, whereas quantization constraint set is defined in the block-based discrete cosine transform (BDCT) domain. Thus, frequent BDCT transform is inevitable in alternative projections. In this paper, based on signal and quantization noise statistics, we proposed a novel smoothness constraint set in the BDCT transform domain via the Wiener filtering concept. Experiments show that POCS using this smoothness constraint set not only has good convergence but also has better objective and subjective performance. Moreover, this set can be used as extra constraint set to improve most existing POCS-based image postprocessing methods.