This paper proposes a new blocking artifact reduction algorithm using an adaptive filter based on classifying the block boundary area. Generally, block-based coding, such as JPEG and MPEG, introduces blocking and ringing artifacts to an image, where the blocking artifact consists of grid noise, staircase noise, and corner outliers. In the proposed method, staircase noise and corner outliers are reduced by a 1D low-pass filter. Next, the block boundaries are divided into two classes based on the gradient of the pixel intensity in the boundary region. For each class, an adaptive filter is applied so that the grid noise is reduced in the block boundary regions. Thereafter, for those blocks with an edge component, the ringing artifact is removed by applying an adaptive filter around the edge. Finally, high frequency components are added to those block boundaries where the natural characteristics have been lost due to the adaptive filter. The computer simulation results confirmed a better performance by the proposed method in both the subjective and objective image qualities.

This paper proposes a new multispectral image data compression algorithm that can efficiently reduce spatial and spectral redundancies by applying classified prediction, a Karhunen-Loeve transform (KLT), and the three-dimensional set partitioning in hierarchical trees (3-D SPIHT) algorithm in the wavelet transform (WT) domain. The classification is performed in the WT domain to exploit the interband classified dependency, while the resulting class information is used for the interband prediction. The residual image data on the prediction errors between the original image data and the predicted image data is decorrelated by a KLT. Finally, the 3-D SPIHT algorithm is used to encode the transformed coefficients listed in a descending order spatially and spectrally as a result of the WT and KLT. Simulation results showed that the reconstructed images after using the proposed algorithm exhibited a better quality and higher compression ratio than those using 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.