Compression Tools Research Articles

Time-recursive computation and real-time parallel architectures: a framework

The time-recursive computation has been proven a particularly useful tool in real-time data compression, in transform domain adaptive filtering, and in spectrum analysis. Unlike the FFT-based ones, the time-recursive architectures require only local communication. Also, they are modular and regular, thus they are very appropriate for VLSI implementation and they allow a high degree of parallelism. In this two-part paper, we establish an architectural framework for parallel time-recursive computation. We consider a class of linear operators that consists of the discrete time, time invariant, compactly supported, but otherwise arbitrary kernel functions. We show that the structure of the realization of a given linear operator is dictated by the decomposition of the latter with respect to proper basis functions. An optimal way for carrying out this decomposition is demonstrated. The parametric forms of the basis functions are identified and their properties pertinent to the architecture design are studied. A library of architectural building modules capable of realizing these functions is developed. An analysis of the implementation complexity for the aforementioned modules is conducted. Based on this framework, the time-recursive architecture of a given linear operator can be derived in a systematic routine way.

Localisation fréquentielle des paquets d'ondelettes

Orthonormal bases of wavelet packets constitute a powerful tool in signal compression. It has been proved by Coifman, Meyer and Wickerhauser that "many" wavelet packets w_n suffer a lack of frequency localization. Using the L^1 -norm of the Fourier transform \hat{w}_n as localization criterion, they showed that the average 2^{–j} \sum^{2^j–1}_{n=0} \|\hat{w}_n\|_{L^1} blows up as j goes to infinity. A natural problem is then to know which values of n create this blowup in average. The present work gives an answer to this question thanks to sharp estimates on \|\hat{w}_n\|_{L^1} which depend on the dyadic expansion of n for several types of filters. Let us point out that the value of \|\hat{w}_n\|_{L^1} is a weak localization criterion, which can only lead to a lower estimate on the variance of \hat{w}_n .

Magnetic Stimulation as a Diagnostic Tool in Cervical Nerve Root Compression and Compression-Induced Neuropathy

Magnetic stimulation of motor cortex and cervical spinal cord was used to assess motor impairment in patients with either cervical nerve root compression or myelopathy. Evoked potentials were recorded from biceps brachii and abductor pollicis brevis. Our interest centered on whether the paresis resulting from root compression is accompanied by an increased latency of magnetically evoked muscle potentials and whether latency is increased in cases of cervical root compression or myelopathy in which no paresis occurs. Latency increase does appear to be a moderately good indicator of disturbed nerve conduction. In particular, prolonged latencies in cases of "subclinical" paresis could be used as an important diagnostic tool for the early detection of motor deficits in cervical compression radiculopathy and myelopathy.

Neural network approaches to image compression

This paper presents a tutor a overview of neural networks as signal processing tools for image compression. They are well suited to the problem of image compression due to their massively parallel and distributed architecture. Their characteristics are analogous to some of the features of our own visual system, which allow us to process visual information with much ease. For example, multilayer perceptions can be used as nonlinear predictors in differential pulse-code modulation (DPCM). Such predictors have been shown to increase the predictive gain relative to a linear predictor. Another active area of research is in the application of Hebbian learning to the extraction of principal components, which are the basis vectors for the optimal linear Karhunen-Loeve transform (KLT). These learning algorithms are iterative, have some computational advantages over standard eigendecomposition techniques, and can be made to adapt to changes in the input signal. Yet another model, the self-organizing feature map (SOFM), has been used with a great deal of success in the design of codebooks for vector quantization (VQ). The resulting codebooks are less sensitive to initial conditions than the standard LBG algorithm, and the topological ordering of the entries can be exploited to further increasing the coding efficiency and reducing the computational complexity.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Application of short-kernel filter pairs for temporal filtering of image sequences

Subband coding of images has received considerable attention and has become an effective tool for image compression. In the work by Karlsson and Vetterli (1988), a 3-D subband coding of video using 2-tap symmetrical short-kernel filters for vertical filtering was suggested for transmission over packet-switched network environments. A modified short-kernel filter pair is proposed to eliminate the coding delay and to improve the reconstructed image quality. Simulations are conducted and satisfactory results are achieved. >

Image compression by vector quantization: a review focused on codebook generation

Recently, vector quantization (VQ) has received considerable attention, and has become an effective tool for image compression. It provides a high compression ratio and a simple decoding process. However, studies on the practical implementation of VQ have revealed some major difficulties such as edge integrity and codebook design efficiency. After reviewing the state-of-the-art in the field of vector quantization, we focus on iterative and non-iterative codebook generation algorithms.

Arithmetic coding of weighted finite automata

Karel Culik and the first author have demonstrated how Weighted Finite Automata (WFA) provide a strong tool for image compression [1, 2, 3]. In the present article we introduce an improved method for the last step of the compression algorithm: for compressing the WFA that approximates the given image. Our method is based on arithmetic coding of sparse matrices

Adaptive Gabor transformation for image processing

An algorithm that computes the Gabor coefficients of an image is presented. An adaptive filter that uses the complex least mean-square algorithm for their computation is proposed, and its numerical characteristics are discussed. It is shown that the filter is stable under certain conditions. Because the Gabor transformation seems to be an excellent tool for image compression, the efficiency of information coding using the Gabor coefficients is investigated and compared with coding that uses the coefficients of the discrete cosine transformation (DCT). Properties of both transformations are discussed.

Neural networks for classified vector quantization of images

Recently, the vector quantization (VQ) technique has received considerable attention and has become an effective tool for data compression. It provides high compression ratios and simple decoding processes for digital images. However, studies of practical implementation of VQ have revealed some major difficulties such as edge integrity of the reconstructed images and computational complexity of codebook design. Over the past few years, a new wave of research in neural networks has emerged. Neural network models have provided an effective means of solving computationally intensive problems. This paper proposes the implementation of classified vector quantization for image compression with neural network models. In order to preserve the edge integrity and improve the efficiency of codebook design, the proposed method includes a multilayer perceptron model for edge classification and a self-organization model for codebook design. A system architecture is proposed, and simulation results demonstrate improvements in visual quality and input encoding.

Wavelet transform as a potential tool for ECG analysis and compression

The recently introduced wavelet transform is a member of the class of time-frequency representations which include the Gabor short-time Fourier transform and Wigner-Ville distribution. Such techniques are of significance because of their ability to display the spectral content of a signal as time elapses. The value of the wavelet transform as a signal analysis tool has been demonstrated by its successful application to the study of turbulence and processing of speech and music. Since, in common with these subjects, both the time and frequency content of physiological signals are often of interest (the ECG being an obvious example), the wavelet transform represents a particularly relevant means of analysis. Following a brief introduction to the wavelet transform and its implementation, this paper describes a preliminary investigation into its application to the study of both ECG and heart rate variability data. In addition, the wavelet transform can be used to perform multiresolution signal decomposition. Since this process can be considered as a sub-band coding technique, it offers the opportunity for data compression, which can be implemented using efficient pyramidal algorithms. Results of the compression and reconstruction of ECG data are given which suggest that the wavelet transform is well suited to this task.

Making Type Inference Practical

We present the implementation of a type inference algorithm for untyped object-oriented programs with inheritance, assignments, and late binding.The algorithm significantly improves our previous one, presented at OOPSLA'91, since it can handle collection classes, such as {\bf List}, in a useful way. Also, the complexity has been dramatically improved, from exponential time to low polynomial time. The implementation uses the techniques of incremental graph construction and constraint template instantiation to avoid representing intermediate results, doing superfluous work, and recomputing type information. Experiments indicate that the implementation type checks as much as 100 lines pr. second. This results in a mature product, on which a number of tools can be based, for example a safety tool, an image compression tool, a code optimization tool, and an annotation tool. This may make type inference for object-oriented languages practical.

Mathematical Tools for Image Restoration and Data Compression

All imaging systems give images of imperfect quality because of degradations caused by various reasons and needs to be restored for computer processing. Compression of image data by using different mathematical techniques without loss of significant information is another necessity for image analysis, understanding and computer vision applications. This paper presents some mathematical algorithms developed by the authors for the above mentioned two objectives, their performances experimentally analysed and are found to be computationally attractive in comparison with the conventional algorithms.

Segmentation of digital curves using linguistic techniques

Techniques for approximating digital curves by polygonal lines are a valuable tool for image analysis and data compression. Here, a syntactical pattern recognition scheme for segmenting a digital curve into connected straight lines is described. The scheme involves only integer operations and can be performed in a single pass. The resulting polygonal line coincides with the digital curve within one pixel unit.

CARPENTER T-K

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