Methods for the Efficient Storage and Manipulation of Spatial Geological Data

The FBI has developed a specification for the compression of gray-scale fingerprint images to support paperless identification services within the criminal justice community. The algorithm is based on a scalar quantization of a discrete wavelet transform decomposition of the images, followed by zero run encoding and Huffman encoding.

This paper proposes a novel grayscale image compression approach using the binary wavelet transform (BWT) and context-based arithmetic coding, namely the context-based binary wavelet transform coding algorithm (CBWTC). In our CBWTC, in order to alleviate the degradation of predictability caused by the BWT and eliminate the correlation within the same level subbands, three highpass wavelet coefficients at the same location are combined to form an octave symbol and then encoded with a ternary arithmetic coder. The conditional context of the CBWTC is properly modeled by exploiting the properties of the BWT as well as taking the advantages of non-causal adaptive context modeling. Experimental results show that the coding performance of the CBWTC is better than that of the state-of-the-art grayscale image coders except for images containing rich texture, and always outperforms the JBIG2 algorithm and other BWT-based binary coding technique.

Lossless image compression and encryption using SCAN

In this study, it analyzed the common evaluating methods for the aerial spraying quality for plant protection in the field experiment. Aiming at the image processing technique to analyze the parameters of droplet deposition in water sensitive image, the disadvantages of the existing systems are discussed. The method of separating the overlapping droplet images by erosion method and calculating the droplet diameter with the image's grayscale depth is proposed. Based on this, the iDAS droplet deposition image processing system was designed. The data collected from the field experiments are compared with the Deposit Scan system developed by USDA and the results show that the system is effective and reliable. The performance of this system is better than Deposit Scan system when analyzing high coverage water-sensitive images. In the analysis of medium coverage and low coverage water-sensitivity images, the performance of these two systems is similar.

In this article we report on a study of how to use the context tree weighting (CTW) algorithm for lossless image compression. This algorithm has been shown to perform optimally, in terms of redundancy, for a wide class of data sources. Our study shows that this algorithm can successfully be applied to image compression even in its basic form. We also report on possible modifications of the basic CTW algorithm to let it work more efficiently for image data. Our research is briefly focussed on the compression of medical gray scale images.

Caveat Spectator: Digital Imaging and Data Manipulation

The FBI has formulated national standards for digitization and compression of gray-scale fingerprint images. The compression algorithm for the digitized images is based on adaptive uniform scalar quantization of a discrete wavelet transform subband decomposition, a technique referred to as the wavelet/scalar quantization method. The algorithm produces archival-quality images at compression ratios of around 15 to 1 and will allow the current database of paper fingerprint cards to be replaced by digital imagery. A compliance testing program is also being implemented to ensure high standards of image quality and interchangeability of data between different implementations. We will review the current status of the FBI standard, including the compliance testing process and the details of the first-generation encoder.

A simple lossy-to-lossless bit-plane coding of still images is presented to integrate several functionality extensions including selective tile partitioning, progressive transmission, ROI transmission, accuracy scalability, and others. The mean squared error between the original image and a decoded image at any progression level is known prior to encoding/decoding. The proposed bit-plane codec is competitive with JPEG-LS and JPEG 2000 in the lossless compression of 8-bit grayscale and 24-bit color images. The codec outperforms the existing standards in 8-bit color-quantized image compression.

On the systematic development of fast fuzzy vector quantization for grayscale image compression

The Interpolation-Based Bintree and encoding of binary images

The linear quadtree is a useful data structure for representing an image for the sake of the storage saving and further image manipulations. In this paper we propose a linear quadtree coding scheme and implement this algorithm on the parallel virtual machine (PVM). Our goal is to demonstrate the applicability of using the PVM in combining the computing power of computers in a network to solve this kind of image processing problems. The processors in the PVM are organized as a master-slave paradigm and various numbers of processors are applied for different PVM’s to compare their performances. Experimental results show that the speedup of solving this image encoding problem in parallel is quite satisfactory. With such a PVM environment which is easily accessible in the public domain, high performance computing is truly possible without additional hardware cost.

Real-time access to large scientific and heterogeneous environmental data has prompted the need for the development of tools allowing efficient data storage, data compression, interactive data browsing, data manipulation, data visualization, searching, and downloading. In this paper we discuss some of the implementation requirements involved in the design of large-scale environmental dataset repositories accessible over the Internet and the World Wide Web. We illustrate this process by discussing the Web tools being developed at the National Supercomputing Center for Energy and the Environment (NSCEE) for the North American Landscape Characterization (NALC) Data Repository Project. A distributed system has been designed and prototyped for delivery of NALC satellite images over the Internet. The NALC Internet Image Server (or SALICIN; anagram of NALCIIS) relieves many of the pains associated with selection and access to 60-m resolution multispectral satellite images of North America for study of human influence on and changes in the environment and ecology over 3 decades (70s, 80s, and 90s). The production image server will provide full data bands, thumbnail browse images (subsampled), and fly-by movies in mpeg and quicktime (not viewable from the CD-ROM).

In this paper we present a Spatial Data Warehouse system that we use for aggregation and analysis of huge amounts of spatial data. The data is generated by utilities meters communicating via radio. In order to provide sufficient efficiency for our system we propose data and workload distribution as well as advanced indexing techniques. The system is based on a cascaded star model, which is a spatial development of a standard star schema and contains interconnected and often nested star schemas. The cascaded star allows efficient storage and analysis of spatial data, whose range extends from meter measurement values to weather information. The indexing tree structure and operation is tightly integrated with the spatial character of the data. Thanks to an available memory evaluating mechanism the system is very flexible in the field of aggregates accuracy. We also implemented indexing structure updating mechanism. The system is written in Java; for the data base we use Oracle 9i. Basing on the wide variety of tests results, we prove that a distributed system significantly surpasses the centralized version in terms of efficiency. We also show that a selective materialization of indexing structure fragments strongly increases system efficiency.

We propose the use of neural network for segmenting character, natural image, and screened halftone image documents. The model network has learned the characteristics of Laplacian histogram of each document. This network consists of three sub-networks and a decisional circuit. The structure of each sub-network is feed forward four layers structured neural network consisting of 10/spl times/10 input cells, 20 first hidden cells, five second hidden cells and two output cells. Those sub-network are the one dividing the documents of character and natural images, the one dividing the documents of natural images and screened halftone images, and the last one dividing the documents of character and screened halftone images. Input patterns to sub-networks composed of 10/spl times/10 data of optical density of pixels having 8 bits' gray levels. The data were processed to have 10% saturation characteristics to the full scales for compressing scratch noises. Furthermore, Laplacian and logarithm operation were adopted to emphasize differences of luminosity distribution between character, natural images, and screened halftone images. An error back propagation method was also used as a learning rule for the sub-networks. As a result, the total network presented successfully more than 90% accuracy of discrimination for each types of document. >

This paper considers the use of data hiding strategies for improved color image compression. Specifically, color information is piggybacked on the luminance component of the image in order to reduce the overall signal storage requirements. A practical wavelet-based data hiding scheme is proposed in which selected perceptually irrelevant luminance bands are replaced with perceptually salient chrominance components. Simulation results demonstrate the improvement in compression quality of the proposed scheme to SPIHT and JPEG at low bit rates. The novel technique also has the advantage that it can be used to further reduce the storage requirements of algorithms such as SPIHT which is optimized for grayscale image compression.