A technique for lossless compression of GMS VISSR images

Author

Yamashita, Kohji ; Yasuda, Akio

Author_Institution

Lab. of Commun. Eng., Tokyo Univ. of Mercantile Marine, Japan

Volume

4

fYear

1994

fDate

8-12 Aug 1994

Firstpage

2200

Abstract

In order to analyze weather conditions, it is useful to get VISSR images of GMS with high brightness resolution. As the data volume is very large, data compression is indispensable to transmit and/or store them. The authors proposed an effective approach for lossless compression of them. The value at each pixel is predicted from the values of pixels around it by means of a linear predictor in the first stage. Then, the error value is obtained as the difference between the real value and predicted one. The error value is again predicted from error values of pixels around it by means of a two-dimensional nonlinear predictor and a residue is obtained as the difference between the error value and prediction one in the second stage. The residues are encoded by Huffman method in the final stage. The data volume of VISSR image can be reduced to 32% by this method. The value is about 20% less than that obtained by the spatial method of JPEG

Keywords

atmospheric techniques; data compression; geophysical signal processing; image coding; optical information processing; remote sensing; GMS; Huffman method; IR imaging; VISSR image; atmosphere meteorology; data compression; image processing; lossless compression; measurement technique; optical imaging; satellite remote sensing; signal processing; two-dimensional nonlinear predictor; Brightness; Data compression; Equations; Etching; Huffman coding; Image analysis; Image coding; Image resolution; Laboratories; Pixel;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 1994. IGARSS '94. Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation., International

Conference_Location

Pasadena, CA

Print_ISBN

0-7803-1497-2

Type

conf

DOI

10.1109/IGARSS.1994.399692

Filename

399692