Jonathan N. Bradley

FBI compression standard for digitized fingerprint images

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.

FBI wavelet/scalar quantization standard for gray-scale fingerprint image compression

The FBI has recently adopted a standard for the compression of digitized 8-bit gray-scale fingerprint images. The standard is based on scalar quantization of a 64-subband discrete wavelet transform decomposition of the images, followed by Huffman coding. Novel features of the algorithm include the use of symmetric boundary conditions for transforming finite- length signals and a subband decomposition tailored for fingerprint images scanned at 500 dpi. The standard is intended for use in conjunction with ANSI/NBS-CLS 1-1993, American National Standard Data Format for the Interchange of Fingerprint Information, and the FBIs Integrated Automated Fingerprint Identification System.

The wavelet/scalar quantization compression standard for digital fingerprint images

A new digital image compression standard has been adopted by the US Federal Bureau of Investigation for use on digitized gray-scale fingerprint images. The algorithm is based on adaptive uniform scalar quantization of a discrete wavelet transform image decomposition and is referred to as the wavelet/scalar quantization standard. The standard produces archival quality images at compression ratios of around 20:1 and will allow the FBI to replace their current database of paper fingerprint cards with digital imagery.<<ETX>>

Reflected boundary conditions for multirate filter banks

Several methods for applying perfect reconstruction quadrature mirror filter (PR QMF) banks to finite-length signals are described and compared. Although simple periodization produces a transform that does not increase the size of the transformed signal, it has the disadvantage of introducing a jump discontinuity at the signals boundary. Various methods of transforming smoother extensions are considered and analyzed in terms of their ability to conserve data storage costs and reproduce the signal in a numerically efficient manner. A complete classification of two-channel schemes based on periodizing symmetric (reflected) signal extensions and using linear phase filters is described, for both even- and odd-length signals. More general techniques based on transforming linear signal extrapolations and truncating the resulting subbands to conserve data size are also presented. An example using reflected boundary extension is discussed.<<ETX>>

Perfect reconstruction with critically sampled filter banks and linear boundary conditions

This work is concerned with the (image) boundary conditions involved in processing a finite discrete-time signal with a critically sampled perfect reconstruction filter bank. It is desirable that the boundary conditions reduce edge effects and define a transformation into a space having the same dimensionality as the original signal. The complication that arises is in the computation of the inverse transform. Although it is straightforward to reconstruct the signal values that were not influenced by the boundary conditions, recovering those values on the boundaries is nontrivial. The solution of this problem is discussed for general linear boundary conditions. No symmetry assumptions are made on the boundary conditions or on the impulse responses of the analysis filters. A low-rank linear transform is derived that expresses the boundary values in terms of the transform coefficients, which in turn provides a method for inverting the subband decomposition. The application of the results in the case of two-channel orthonormal wavelet filters is discussed, and the effects of the filter support on the conditioning of the inverse problem are investigated. >

Wavelet transform-vector quantization compression of supercomputer ocean models

A new procedure for efficient compression of digital information for storage and transmission purposes involves a discrete wavelet transform subband decomposition of the data set, followed by vector quantization of the wavelet transform coefficients using application-specific vector quantizers. The vector quantizer design optimizes the assignment of both memory resources and vector dimensions to the transform subbands by minimizing an exponential rate-distortion functional subject to constraints on both overall bit-rate and encoder complexity. The method is applicable to the compression of other multidimensional data sets possessing some degree of smoothness. The authors discuss the use of this technique for compressing the output of supercomputer simulations of global climate models. The data presented here comes from Semtner-Chervin global ocean models run at the National Center for Atmospheric Research.<<ETX>>

Suppression of adjacent-channel and cochannel FM interference via extended Kalman filtering

The problem of demodulating an FM signal in the presence of an adjacent-channel or cochannel interferer is addressed. The pertinent measurement equation and a first-order state-space model of the underlying message processes are incorporated in an optimal estimation framework. Due to the nonlinearity of the measurement equation the extended Kalman filter is used. The resulting system consists of two interconnected second-order phase-locked loops (PLLs), each of which tracks one of the two FM signals. The system is contrasted with the previous state of the art in the area, which combined maximum a posteriori estimation with adaptive filtering and also yielded a cross-coupled PLL structure. The significant differences in the new system are variable loop gains, a different amplitude estimator structure, and an interconnection between the internal states of the PLLs. Computer simulation results are presented comparing the relative effectiveness of the Kalman filter and the maximum a posteriori estimator.<<ETX>>

Image compression by vector quantization of multiresolution decompositions

Abstract An image compression scheme is introduced which involves a multiresolution decomposition derived from the wavelet transform. The use of this transformation in image coding is motivated by its similarity to the processing which occurs at the cortical level of the visual system and by the fact that subband coding in general allows the coder to be better matched to the signal statistics. In this work, each subband is coded separately by vector quantization. This work differs from previous wavelet transform coding schemes involving vector quantization in that the quantization parameters for each subband are selected by a nonlinear optimization procedure which involves constraints on the overall bit-rate and the encoding complexity. The multiplicative visual model is reviewed and a motivation for cascading this system with the wavelet transform to give a more complete description of human vision is provided. Empirical results are presented which demostrate the effectiveness of the technique for compressing monochromatic images. Signal-to-noise ratio measurements are shown which demostrate the system performance for various selections of bit-rate and complexity.

SPECTRUM analysis of multispectral imagery in conjunction with wavelet/KLT data compression

The data analysis program, SPECTRUM, is used for fusion, visualization, and classification of multispectral imagery. To facilitate data transmission and storage, a compression algorithm is proposed based on spatial wavelet transform coding and KLT decomposition of interchannel spectral vectors, followed by adaptive optimal multiband scalar quantization. The performance of SPECTRUM clustering and visualization is evaluated on compressed multispectral data. 8-bit visualizations of 56-bit data show little visible distortion, at 50:1 compression and graceful degradation at higher compression ratios.<<ETX>>