Showing papers on "Vector quantization published in 1978"
TL;DR: The optimum (minimum mean-squared-error criterion) and optimum uniform quantizer characteristics for signals characterized by the Laplacian amplitude probability density function are given in tabular form.
Abstract: The optimum (minimum mean-squared-error criterion) and optimum uniform quantizer characteristics for signals characterized by the Laplacian amplitude probability density function are given in tabular form. These results correct and extend previously published results.
55 citations
TL;DR: Methods for predicting optimum quantization schemes and associated mean squared errors are simplified to table-lookup procedures.
Abstract: Finite plotter resolution introduces quantization into the representation of digital holograms. By utilizing the statistical properties of the Fourier transform of random phase images, optimum quantization schemes are derived and tabulated for the representation of these transforms. These schemes are applied to Lohmann and Lee type holograms where it is found that measured quantization errors agree with theoretical predictions. Methods for predicting optimum quantization schemes and associated mean squared errors are simplified to table-lookup procedures.
35 citations
TL;DR: It is shown that the proposed robust quantizer has properties exhibited by established robust procedures, and results are presented for the case of contaminated Gaussian noise for various degrees of contamination.
Abstract: The problem of designing quantizer-detectors whose performance is insensitive to small deviations in noise statistics is considered. The problem is approached on a small-signal asymptotic basis using the Huber-Tukey [1] contaminated density class to model the noise. By applying a formulation originally noted by Martin and Schwartz, it is shown that the proposed robust quantizer has properties exhibited by established robust procedures. As an example, results are presented for the case of contaminated Gaussian noise for various degrees of contamination.
21 citations
1 Jan 1978
8 citations
7 Dec 1978
TL;DR: A DPCM technique that uses a fixed-error approach to minimize the loss of information in compression of a picture and is compared to three other encoding schemes, including a new, "one-pass", cosine transform encoder.
Abstract: We present a DPCM technique that uses a fixed-error approach to minimize the loss of information in compression of a picture. The technique uses an initial N-bit quantization of the image and zero-error encoding of the difference signal. It produces no slope overload and a compression ratio of about 4 to 1. We compare the technique to three other encoding schemes, including a new, "one-pass", cosine transform encoder.© (1978) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
2 citations
TL;DR: Though orthogonal quantization is inferior to optimal quantization, it is essentially simpler and does not incur great loss in performance.
Abstract: Orthogonal quantization is a partition of signal space that is achieved by independent quantization of each of its M orthogonal axes. A closed form expression is derived for the quantized channel cutoff rate and for the optimum orthogonal quantization similar to the one that has been derived for binary signaling. While orthogonal quantization is natural for communication systems in which the transmitted signals are themselves orthogonal, it can also be profitably applied to other signals, e.g., a simplex set in a lower dimensional space. Though orthogonal quantization is inferior to optimal quantization, it is essentially simpler and does not incur great loss in performance. A numerical example illustrates the relative merits of optimal and orthogonal quantization for the simplex set in the plane.
1 citations
TL;DR: Quantization is the process of replacing analog samples with approximate values taken from a finite set of allowed values.
Abstract: Quantization is the process of replacing analog samples with approximate values taken from a finite set of allowed values. The approximate values corresponding to a sequence of analog samples can then be specified by a digital signal for transmission, storage, or other digital processing. In this expository paper, the basic ideas of uniform quantization, companding, robustness to input power level, and optimal quantization are reviewed and explained. The performance of various schemes are compared using the ratio of signal power to mean-square quantizing noise as a criterion. Entropy coding and the ultimate theoretical bound on block quantizer performance are also compared with the simpler zero-memory quantizer.
TL;DR: The degradation of the signal-to-noise ratio of signals quantized with one, two, or three bits and matched with the correlation function and sequential similarity detection algorithm is investigated.
Abstract: The degradation of the signal-to-noise ratio of signals quantized with one, two, or three bits and matched with the correlation function and sequential similarity detection algorithm is investigated. Except for the one-bit case, the correlation function produces a better signal-to-noise ratio for the low-level quantizers considered.
TL;DR: The criterion based on absolute error is shown to have unique properties, justifying its use as a performance index for optimum quantization and the implication for adaptive quantization of the use of this criterion is discussed.
Abstract: Performance criteria for the design of optimum quantizers are considered. A distance criterion for quantizer input and output probability distribution functions is formulated, and its relationship to the usual distortion criteria is established. The criterion based on absolute error is shown to have unique properties, justifying its use as a performance index for optimum quantization. Numerical results are presented, and the implication for adaptive quantization of the use of this criterion is discussed.