A postprocessing algorithm is presented for blocking artifact reduction in block-coded images using the adaptive filters along the pattern of neighborhood blocks. Blocking artifacts appear as irregular high-frequency components at block boundaries, thereby reducing the noncorrelation between blocks due to the independent quantization process of each block. Accordingly, block-adaptive filtering is proposed to remove such components and enable similar frequency distributions within two neighborhood blocks and a high correlation between blocks. This type of filtering consists of inter-block filtering to remove blocking artifacts at the block boundaries and intra-block filtering to remove ringing noises within a block. First, each block is classified into one of seven classes based on the characteristics of the DCT coefficient and MV (motion vector) received in the decoder. Thereafter, adaptive intra-block filters, approximated to the normalized frequency distributions of each class, are applied adaptively according to the various patterns and frequency distributions of each block as well as the filtering directions in order to reduce the blocking artifacts. Finally, intra-block filtering is performed on those blocks classified as complex to reduce any ringing noise without blurring the edges. Experimental tests confirmed the effectiveness of the proposed algorithm.

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 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.

This paper proposes a reliable data aggregation scheduling that uses caching and re-transmission based on track topology. In the proposed scheme, a node detects packet losses by overhearing messages that includes error indications of the child nodes, from its neighbor nodes. If packet losses are detected, as a backup parent, the node retransmits the lost packet. A retransmission strategy is added into the adaptive timeout scheduling scheme, which adaptively configures both the timeout and the collection period according to the potential level of an event occurrence. The retransmission steps cause an additional delay and power consumption of the sensor nodes, but dramatically increase the data accuracy of the aggregation results. An extensive simulation under various workloads shows that the proposed scheme outperforms other schemes in terms of data accuracy and energy consumption.

The current paper presents an effective deblocking algorithm for block-based coded images using singularity detection in a wavelet transform. Blocking artifacts appear periodically at block boundaries in block-based coded images. The local maxima of a wavelet transform modulus detect all singularities, including blocking artifacts, from multiscale edges. Accordingly, the current study discriminates between a blocking artifact and an edge by estimating the Lipschitz regularity of the local maxima and removing the wavelet transform modulus of a blocking artifact that has a negative Lipschitz regularity exponent. Experimental results showed that the performance of the proposed algorithm was objectively and subjectively superior.

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.