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Showing papers on "Quantization (image processing) published in 1982"


Journal ArticleDOI
TL;DR: Algorithms for adaptive, tapered quantization of color images are described and the research is motivated by the desire to display high-quality reproductions ofcolor images with small frame buffers.
Abstract: Algorithms for adaptive, tapered quantization of color images are described. The research is motivated by the desire to display high-quality reproductions of color images with small frame buffers. ...

477 citations


Journal ArticleDOI
TL;DR: Application of the closed contour extraction method to meteorological satellite images proved to be successful while classical methods would have failed.

5 citations


Proceedings ArticleDOI
01 May 1982
TL;DR: The results of the study indicate that through the use of adaptive prediction and quantization, a high level of image fidelity can be obtained for both intensity and density images at information rates well below one bit/pixel.
Abstract: This paper is a preliminary report on a study of the application of two-dimensional linear prediction in image quantization. The study has focused on three major concerns: implementation of an adaptive linear predictor, adaptive quantization of the prediction error signal, and the adaptive predictive coding of density (logarithm of intensity) images. The results of the study indicate that through the use of adaptive prediction and quantization, a high level of image fidelity can be obtained for both intensity and density images at information rates well below one bit/pixel.

4 citations


Proceedings ArticleDOI
01 Nov 1982
TL;DR: This paper analyzes phase-only reconstruction, considers bit-rate reduction both in sampling and quantization, and develops the resolution enhancement algorithm associated with the phase- only technique.
Abstract: Data acquisition and image reconstruction in acoustical imaging often requires high bit rates and complicated calculations in order to achieve good quality images in reasonable time. Therefore reducing the bit rates and simplifying the reconstruction algorithms are of great importance in this technology. It is possible to produce high-quality images by using only the phase information of the detected signal. The phase-only technique constitute a way of performing high-quality imaging at lower bit rates. This paper analyzes phase-only reconstruction, considers bit-rate reduction both in sampling and quantization, and develops the resolution enhancement algorithm associated with the phase-only technique.

4 citations


Patent
13 Sep 1982
TL;DR: In this article, a difference processing circuit is proposed to improve the quality of pictures to be processed, by constituting the 1st and 2nd difference processing circuits in such a way that they can be operated in parallel.
Abstract: PURPOSE:To improve the quality of pictures to be processed, by constituting the 1st difference processing circuit and the 2nd difference processing circuit in such a way that they can be operated in parallel. CONSTITUTION:A difference processing circuit 101 implements difference values between toward picture elements expanded in parallel at 2, and outputs the difference value while the size of the difference value is limited by a prescribed limit value. A memory 11 stores a forecasting signal against the 1st picture element value among the toward-picture elements, and subtracts the output value of the memory 11 from the 1st picture element value, and then, a difference processing circuit 102 which outputs the difference value while the difference value is limited with a prescribed limit value. The output of both the circuits 102 and 101 are inputted into a toward-quantization circuit 103, and quantization is performed based on a preset quantization characteristic. Moreover, the output of the circuit 103 is added to that of the memory 11 at an adder 10 and the value is supplied to the memory 11.

1 citations


Book ChapterDOI
Theo Pavlidis1
01 Jan 1982
TL;DR: This chapter shall devote the first part of this chapter to a review of transform techniques and then the next part to discuss sampling for the one-dimensional case, followed by sampling for pictures, which will be treated in the last section.
Abstract: When a picture is to be processed by computer, it is often described as a matrix, or some other discrete data structure. But a picture is primarily a signal that conveys information to an observer, and there are many applications where this consideration is particularly important. We shall devote this and the next chapter to the discussion of such problems, especially for gray scale (class 1) images. The first problem is the conversion of a continuous picture into a discrete form, and this involves two processes: sampling, which is the selection of a discrete grid to represent an image, and quantization, which is the mapping of the brightness and color values into integers. In graphics one is concerned with similar problems: specifically the choice of the display resolution and number of gray levels or colors. These processes are also relevant in one-dimensional data and have been studied thoroughly in that case, but two-dimensional data present new problems. We shall devote the first part of this chapter to a review of transform techniques and then we shall discuss sampling for the one-dimensional case, followed by sampling for pictures. Quantization will be treated in the last section.

1 citations