Blocking artifacts reduction algorithm in block boundary area using neural network

A novel algorithm is proposed to reduce the blocking artifacts of block-based coded images by using block classification and feedforward neural network filters in the spatial domain. In the proposed algorithm, we classify the block boundaries into four classes based on a statistical characteristic of neighborhood block. For each class, adaptive inter-block filtering performs in block boundaries. That is, for the smooth region with blocking artifacts, we use the two-layer neural network with error backpropagation algorithm, and for the complex region with blocking artifacts, we use the linear interpolation method. Experimental results show that the proposed algorithm gives better results than the conventional algorithms from both the subjective and objective viewpoint.