Blocking effect reduction of compressed images using classification-based constrained optimization

In this paper we propose an adaptive image restoration algorithm using block-based edge-classification for reducing block artifacts in compressed images. In order to efficiently reduce block artifacts, edge direction of each block is classified by using model-fitting criterion, and the constrained least-squares (CLS) filter with corresponding direction is used for restoring the block. The proposed restoration filter is derived based on the observation that the quantization operation in a series of coding processes is a nonlinear and many-to-one mapping operator. Then we propose an approximated version of a constrained optimization technique as a restoration process for removing the nonlinear and space-varying degradation operator. For real-time implementation, the proposed restoration filter can be realized in the form of a truncated FIR filter, which is suitable for postprocessing reconstructed images in digital TV, video conferencing systems, etc.