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This paper proposes the adaptive discrete cosine transform coding with vector quantization (adaptive DCT-VQ), as a highly efficient coding for the picture signal. In the proposed method, the vector quantization is performed by applying the discrete cosine transform to the picture subblock and partitioning the transformed domain into several vectors. By regarding the transformed domain as more than one vector, the complexity in the coding is reduced. Each vector quantizer is designed according to the standard probability distribution of the cosine transform coefficient, and the universality of the vector quantizer is realized. The mode-selecting type adaptive control is introduced, to effect the adaptive control of the coding parameters according to the local property of the picture. To cope with the large bit allocation resulting from the adaptive control, each vector quantizer has two-stage structure thereby reducing the coding complexity. As a result of the performance evaluation by simulation, it was verified that the proposed coding method has a higher performance than the conventional adaptive cosine transform coding and has much less complexity than the vector quantization in the original space domain. [ABSTRACT FROM AUTHOR]

A digital filter is developed to estimate signals containing discontinuities from observations contaminated with low-amplitude white noise. Called an &epsilon-Kalman filter, this is a Kalman filter combined with a nonlinear function. It is very effective for signals with known statistical properties. Principles of operation and realization of the filter are discussed. It is compared with another nonlinear filter called an ε-filter also proposed by the authors. Results of computer simulations are shown to demonstrate the effectiveness of the new filter. Pictorial examples are also provided. [ABSTRACT FROM AUTHOR]

This paper presents a new design method for a frequency sampling adaptive equalizer using the least-squares method and reports the results obtained in analyzing the equalizer performance. This system employs an adaptive equalizer configuration in which a cyclic code equal to the tap length is used as a test signal with the received signal and the reference signal each undergoing a discrete Fourier transformation using a frequency sampling filter and applying the least-squares method in a one-to-one relationship to each DFT coefficient. A condition for the implementation of this system is that the test signal be a cyclic code, but this system requires fewer arithmetic operations than any other system employing the least-squares method. The system presented here employs a high-speed convergence algorithm in the initial training period and after convergence is accomplished it switches to a gradient method using a frequency sampling adaptive equalizer. In simulation tests with telephone lines it was found that this equalizer converged in about one-third the time required for the transversal adaptive equalizer. [ABSTRACT FROM AUTHOR]

In a previous paper we presented procedures of analyzing causality between a time series based on information theory and information flow between time series based on a concept of directed transinformation, which is an information quantity with a direction. In principle, the directed transinformation is defined based on joint probability density between time series. As the length of the time series becomes larger, however, the volume of computation needed also increased exponentially. In this paper, we will discuss efficient computation algorithms. First, we assume Gaussian properties with the time series to present the "correlation function method" as a means of computing of the directed transinformation from correlation function. Second, we present a "linear production model method" in which the information quantity is computed by means of impulse response of a linear production model of the time series. Also, we proved that the linear production model can easily be composed by auto-regression if the time series is steady. Finally, we discussed the validity of time-series analysis based on the directed transinformation through analysis of an artificially produced simulated series. We have concluded that the proposed computation method can provide correct analysis even if the causality of the time series can not be analyzed correctly based on the correlation function. [ABSTRACT FROM AUTHOR]

For high-efficiency image compression, previously, an SVD (singular value decomposition)-based coder was developed using vector quantization, called SVD-VQ. This paper proposes an improved quantization SVD-VQ scheme. For every input subblock, the SVD- VQ coder scalar-quantizes a singular value and vector-quantizes two singular vectors, separately. The SVD-VQ decoder reproduces a subblock as the product of these quantization outputs, but does not necessarily produce a reconstruction with the minimum distortion in an image space. This paper develops a quantization scheme where the minimum-distortion reconstruction is always provided in the original image space and presents its design algorithm. The improved SVD-VQ shows A/N performance improvement of 0.5 - 1.0 dB over the conventional SVD-VQ, and is similar in performance to the adaptive DCT (discrete cosine transform) coder. [ABSTRACT FROM AUTHOR]

To realize fast search at vector quantization applied to high-efficiency coding of signals, we propose a partial distance algorithm and a nearest-neighbor search algorithm, both in the orthogonal transform domain. These algorithms provide efficient search for signals such as, for example, picture signals which have a concentration in the transform domain power distribution. Further, we propose a nearest-neighbor search algorithm in orthogonal transform domain with component selection which is effective also for memoryless random information sources with Gaussian distribution. The proposed algorithm is applied to vector quantizers of picture signals, separating-mean-normalized vector quantizers, and vector quantizers of memoryless Gaussian sources and its efficiency is demonstrated for these cases. Particularly, in the case of vector quantizers of picture signals, it is shown that the proposed method requires fewer multiplications to determine the optimum reconstructing vector than the conventional tree search algorithm. [ABSTRACT FROM AUTHOR]

The authors have previously proposed a quantization scheme in which the ac discrete cosine transform coefficients after normalization are quantized by the gain/shape vector quantizer (called DCT-G/SVQ). It is shown from the relation between the quantization complexity and the S/N ratio that DCT-G/SVQ exhibits a better performance than the discrete cosine transform coding with vector quantization (called DCT-VQ) discussed in the literature [6]. This paper proposes the adaptive DCT-G/SVQ, in which the adaptive control is introduced £or the coding parameters of DCT-G/SVQ. Two methods of adaptive control are considered--the mode- selection and the recursive adaptation. The adaptive DCT-G/SVQ has a better performance than the traditional adaptive discrete cosine transform coding using the scalar quantizer. In other words, the S/N ratio is improved by 1.0 to 1.5 dB in the mode-selection adaptive control coding at the same coding rate. The S/N ratio in the recursive adaptive control coding is improved by 0.5 to 1.0 dB. The adaptive DCT-G/SVQ based on the mode-selection control exhibits nearly the same coding performance as the adaptive DCT-VQ of [9] with the same adaptive control scheme, but is superior since it requires less complexity in quantization. [ABSTRACT FROM AUTHOR]

A gain/shape vector quantizer for orthogonal transform domain has been designed based on a previous paper [11] on a gain/shape vector quantizer for a spherically symmetric random source. Such a design method is used in the design of a discrete cosine transform encoder with gain/shape vector quantizer. This encoder is similar to that described in [7] except in the following: (i) it has a gain/shape codebook; and (ii) its normalized coefficients of discrete cosine transform are modeled as having spherically symmetric distribution. The S/N rendition of this encoder is 1.0 - 2.5 dB better than that of the DCT coding scheme with Max-type scalar quantizer at the same bit rate. Furthermore, in spite of its similarity to the conventional discrete cosine transform coding scheme with vector quantizer [7], the encoder introduced in this paper delivers higher S/N using the same number of multiplications for the quantization of normalized coefficients, and/or the same amount of memory for the codebook. [ABSTRACT FROM AUTHOR]

In a picture data base the picture quality required depends on the actual application of the picture signal. Therefore efficient reversible coding systems are needed. On the other hand, users demand easy searching of the required pictures. Considering the above facts, two kinds of information preserving transmission systems-System 1 and System 2-are proposed for picture data base treating 8-bit PCM picture signals. In these systems, first, reference picture signal for user's reference are transmitted. Next, a supplementary transmission is performed. In System 1 a degenerate differential signal between PCM picture signal and reference picture signal is encoded and transmitted. In system 2, PCM picture signal is reversibly encoded and transmitted directly. In this paper, four kinds of coding systems are considered as coding system for reference pictures. The best performance has been achieved by discrete cosine transform pictures. The best performance has been achieved by discrete cosine transform coding. In addition, this paper presents a method for determining the better system in performance of supplementary transmission of the two systems on the basis of signal-to-noise ratio of the reference picture. Finally, the realization of a transmission systems is shown which combines the advantages of both Systems 1 and 2 by choosing the better system on the basis of coding performance at each reference picture. [ABSTRACT FROM AUTHOR]

The probability density of normalized ac orthogonal transform coefficients of image data can be modeled as a spherically symmetric distribution. On the other hand, the gain/shape vector quantizer [8] proposed by Buzo et al. is expected to provide a good quantization performance and a flexible design method for spherically symmetric random source. Therefore, it may be possible to realize a flexible vector quantizer by designing a gain/shape vector quantizer in the orthogonal transform domain. With this expectation the asymptotic performance and optimal allocations of the number of quantization levels of the gain/shape vector quantizer for an n-dimensional spherically symmetric random source was derived and it was shown that for n ≤ 10 the gain/shape vector quantizer yields the asymptotic performance between its upper bounds derived y Zador's [10] and those by Conway and Sloane [11], and that for n > 11 its asymptotic performance is similar to the Zador's upper bounds. Also, comparison with the asymptotic performance of scalar quantizers [12] was made and it was shown that possible improvement of coding performance can be obtained by applying the gain/shape vector quantizer to orthogonal transform image conding, for which conventionally scalar quantizers have been used. Furthermore, a simple and flexible design method for the suboptimal gain/shape vector quantizer for a given spherically symmetric distribution was presented. [ABSTRACT FROM AUTHOR]