Reduction of coding artifacts in transform image coding

The problem of image decompression is cast as an ill-posed inverse problem, and a stochastic regularization technique is used to form a well-posed reconstruction algorithm. A statistical model for the image which incorporates the convex Huber minimax function is proposed. The use of the Huber minimax function rho T(.) helps to maintain the discontinuities from the original image which produces high-resolution edge boundaries. Since rho T(.) is convex, the resulting multidimensional minimization problem is a constrained convex optimization problem. The maximum a posteriori (MAP) estimation technique that is proposed results in the constrained optimization of a convex functional. The proposed image decompression algorithm produces reconstructed images which greatly reduced the noticeable artifacts which exist using standard techniques.<>