A novel postprocessing algorithm for concealing spatial block errors in block-based coded images is proposed using block classification with a variable operating region (VOR). In the proposed algorithm, a missing block is classified as flat, edge, or complex based on local information from the surrounding blocks which is extracted using a Sobel operation in a VOR. In this case, the VOR is determined adaptively according to the number of edge directions in the missing block. Using the classification, the flat blocks are then concealed by the linear interpolation (LI) method, the edge blocks are concealed by the boundary multi-directional interpolation (BMDI) method, and the complex blocks are concealed by a combined linear interpolation and boundary matching (CLIBM) method. Experimental results demonstrated that the proposed algorithm improved the PSNR and visual quality of the concealment for both original images and JPEG compressed images, and produced better results than conventional algorithms.

A novel postprocessing algorithm for reducing the blocking artifacts in block-based coded images is proposed using block classification and adaptive multi-layer perceptron (MLP). This algorithm is exploited the nonlinearity property of the neural network learning algorithm to reduce the blocking artifacts more accurately. In this algorithm, each block is classified into four classes; smooth, horizontal edge, vertical edge, and complex blocks, based on the characteristic of their discrete cosine transform (DCT) coefficients. Thereafter, according to the class information of the neighborhood block, adaptive neural network filters (NNF) are then applied to the horizontal and vertical block boundaries. That is, for each class a different two-layer NNF is used to remove the blocking artifacts. Experimental results show that the proposed algorithm produced better results than conventional algorithms both subjectively and objectively.

A new blocking artifact reduction algorithm is proposed that uses block classification and feedforward neural network filters in the spatial domain. At first, the existence of blocking artifact is determined using statistical characteristics of neighborhood block, which is then used to classify the block boundaries into one of four classes. Thereafter, adaptive inter-block filtering is only performed in two classes of block boundaries that include blocking artifact. That is, in smooth regions with blocking artifact, a two-layer feedforward neural network filters trained by an error back-propagation algorithm is used, while in complex regions with blocking artifact, a linear interpolation method is used to preserve the image details. Experimental results show that the proposed algorithm produces better results than the conventional algorithms.

In this paper we introduce a new feature called stroke index for document image analysis. It is based on the minimum covering run expression method (MCR). This stroke index is a function of the number of horizontal and vertical runs in the original image and of number of runs by the MCR expression. As document images may present a variety of patterns such as graph, text or picture, it is necessary for image understanding to classify these different patterns into categories beforehand. Here we show how one could use this stroke index for such applications as classification or segmentation. It also gives an insight on the possibility of stroke extraction from document images in addition to classifying different patterns in a compound image.