In the study, an efficient compression approach, group and region based KLT (GR-KLT), is proposed for hyperspectral imagery. The GR-KLT contains one clustering signal subspace projection (CSSP) segmentation method and the maximum correlation band clustering (MCBC) method. The CSSP first divides the image into proper regions and the MCBC partitions the spectral bands into several groups according to their associated band correlation for each image region. By the way, the image is further compressed by the KLT-JPEG for each group in each image region. Furthermore, we develop a parallel architecture for the GR-KLT compression algorithm. Simulation results performed on AVIRIS images have demonstrated the efficiency of the proposed approaches.

In the study, we propose a novel unsupervised classification technique for hyperspectral images, which consists of two algorithms, referred to as the maximum correlation band clustering (MCBC) and hierarchical binary quaternion-moment-preserving (BQMP) thresholding technique. By the MCBC, we partition the bands into groups and transfer the high-dimensional image data into low-dimensional image features. Afterwards, the hierarchical BQMP approach partitions the feature image into proper regions according to the spectral characteristics. Simulation results performed on AVIRIS images have demonstrated the efficiency of the proposed approaches.

In the study, we proposed two adaptive classifiers based on image eigen-features for multispectral image classification. An adaptive signal subspace projection (ASSP) approach is first proposed to detect and extract target signatures in unknown background. The weights of ASSP are adjusted adaptively by using the eigen-features which are updated recursively by the adaptive eigen-decomposition algorithm. Then, we proposed an artificial neural networks (ANN) based on back propagation multilayer perception (BPMLP) with weights trained by the image eigen-features. Simulation results validate the image eigen-features can alleviate the noise effect in classification and the proposed ASSP and BPMLP classifiers have lower detection error and fast convergence rate than conventional Wiener filter and per-pixel ANN methods.

In the study, we present a novel indexing technique for the retrieval of satellite image databases. The approach partitions the original image into some eigen-regions according to local terrain characteristics. And index for each eigen-region, called eigen-index, is extracted by the principal eigenvector of region. Using the eigen-index separation between the specified cover type of query image and that of each object image in the database, the access of archive could be facilitated. To show the performance of the proposed approach, a PHP-based interface agent using the eigen-index for content-based access to a test archive of SPOT images is available over the internet [http://dsp.mmd.ntou.edu.tw]. Through this agent, a user can browse ten best-matched candidates for each query.

Many display devices now allow a number of colors, called color palette, to be displayed simultaneously. Besides, images and videos in most world wide web (WWW) databases are in compressed formats. Therefore, it becomes an important issue to retrieve a suitable color palette from compressed domain in order to have fast and faithful color reproduction for these devices. In this paper, the color palette design methods for compressed videos are presented. The proposed approaches use the reduced image rather than the whole image for the color palette design to avoid the heavy computation in video decompression. Experimental results show that output image quality of proposed methods is acceptable, to human eyes. In addition, empirical results show that the proposed shifting window scheme can reduce the main problem of displaying quantized image sequences and screen flicker.

Many display devices nowadays still allow a limited number of colors, called color palette, to be displayed simultaneously. Besides, images and videos in most World Wide Web databases are in compressed formats. Therefore, it becomes an important issue to retrieve a suitable color palette from compressed domain in order to have fast and faithful color reproduction for these devices. In this paper, the color palette design methods for compressed images and videos are presented. The proposed approaches use the reduced, rather than the whole, image for the color palette design to avoid the heavy computation in image or video decompression. Also, for compressed videos, a shifting-window scheme is proposed to smooth out color variations in the change of color palette. In these methods, we extend the dependent scalar quantization algorithm of a single image to accomplish the color palette design. Experimental results show that the output image quality of the proposed methods is acceptable to human eyes. In addition, empirical results show that the proposed shifting-window scheme can reduce the main problem of displaying quantized image sequences, screen flicker.

Color-based indexing is an important tool in image database retrieval. Compared with other features of the image, color features are less sensitive to noise and background complication. Based on the human visual system's perception of color information, this paper presents a dependent scalar quantization approach to extract the characteristic colors of an image as color features. The characteristic colors are suitably arranged in order to obtain a sequence of feature vectors. Using this sequence of feature vectors, a dynamic matching method is then employed to match the query image with database images for a nonstationary identification environment. The empirical results show that the characteristic colors are reliable color features for image database retrieval. In addition, the proposed matching method has acceptable accuracy of image retrieval compared with existing methods.

This paper presents a new moment-preserving thresholding technique, called the binary quaternion-moment-preserving (BQMP) thresholding, for color image data. Based on representing color data by the quaternions, the statistical parameters of color data can be expressed through the definition of quaternion moments. Analytical formulas of the BQMP thresholding can thus be determined by using the algebra of the quaternions. The computation time for the BQMP thresholding is of order of the data size. By using the BQMP thresholding, quaternion-moment-based operators are designed for the application of color image processing, such as color image compression, multiclass clustering of color data, and subpixel color edge detection. The experimental results show that the proposed operator for color image compression can have output picture quality acceptable to human eyes. In addition, the proposed edge operator can detect the color edge at the subpixel level. Therefore, the proposed BQMP thresholding can be used as a tool for color image processing.

Block truncation coding (BTC) is an efficient tool for image compression. To compress color-pixel blocks, a novel color BTC algorithm, called quaternion-moment block truncation coding (QMBTC), is presented. Analytical formulas for QMBTC, whose computation time is on the order of pixel block size, are derived by using quaternion arithmetic and the moment-preserving principle. The proposed color BTC algorithm can adaptively truncate a pixel block into one or two output classes according to the distribution of the color values inside the blocks. The experimental results show that the compression ratio is increased as compared with existing color BTC algorithms, and the picture quality of the reconstructed images is satisfactory. In addition, a post-BTC data compression scheme is proposed to further compress the subimage constructed by reproduction colors of truncated pixel blocks. Using a lookup table to display decoded data, this postprocessing scheme can output images acceptable to human eyes.

Many image display devices can allow only a limited number of colors, called color palette, to be simultaneously displayed. In order to have a faithful color reproduction of an image, the associated color palette must be suitably designed. This paper presents a dependent scalar quantization algorithm to design the color palette effectively. The dependent scalar quantization algorithm consists of two procedures, bit allocation and recursive binary moment preserving thresholding. The experimental results show that the dependent scalar quantization can reduce the computation complexity and its output images quality is acceptable to the human eyes. A rule of the quantization order is also deduced under the MSE criterion to obtain a dependent scalar quantizer which has as good a performance as compared with some other algorithms. In addition, an adaptive neighborhood-clustering algorithm, which searches the neighboring color indices of input pixels iteratively, is proposed to further improve the performance of the dependent scalar quantization algorithm. Finally, we introduce a color mapping method to reduce the contouring effect when the color palette size generated by the dependent scalar quantizer is small.>