COLOR SATELLITE IMAGE COMPRESSION USING THE EVIDENCE THEORY AND HUFFMAN CODING
K. SAHNOUN, and N. BENABADJI. The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE), 1 (1):
7(April 2014)
Abstract
The color satellite image compression technique by vector quantization can be improved either by acting
directly on the step of constructing the dictionary or by acting on the quantization step of the input vectors.
In this paper, an improvement of the second step has been proposed. The k-nearest neighbor algorithm was
used on each axis separately. The three classifications, considered as three independent sources of
information, are combined in the framework of the evidence theory. The best code vector is then selected,
after the image is quantized, Huffman schemes compression is applied for encoding and decoding
%0 Journal Article
%1 noauthororeditor
%A SAHNOUN, Khaled
%A BENABADJI, Noureddine
%D 2014
%E AIRCC,
%J The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE)
%K Huffman Vector character coding, evidence k-nearest neighbor, quantization,compression, theory.
%N 1
%P 7
%T COLOR SATELLITE IMAGE COMPRESSION USING THE EVIDENCE THEORY AND HUFFMAN CODING
%U http://airccse.com/ijcsitce/papers/1114ijcsitce05.pdf
%V 1
%X The color satellite image compression technique by vector quantization can be improved either by acting
directly on the step of constructing the dictionary or by acting on the quantization step of the input vectors.
In this paper, an improvement of the second step has been proposed. The k-nearest neighbor algorithm was
used on each axis separately. The three classifications, considered as three independent sources of
information, are combined in the framework of the evidence theory. The best code vector is then selected,
after the image is quantized, Huffman schemes compression is applied for encoding and decoding
@article{noauthororeditor,
abstract = {The color satellite image compression technique by vector quantization can be improved either by acting
directly on the step of constructing the dictionary or by acting on the quantization step of the input vectors.
In this paper, an improvement of the second step has been proposed. The k-nearest neighbor algorithm was
used on each axis separately. The three classifications, considered as three independent sources of
information, are combined in the framework of the evidence theory. The best code vector is then selected,
after the image is quantized, Huffman schemes compression is applied for encoding and decoding},
added-at = {2017-11-22T07:22:13.000+0100},
author = {SAHNOUN, Khaled and BENABADJI, Noureddine},
biburl = {https://www.bibsonomy.org/bibtex/21e73a14a08a43a7b6d7aec6056363512/suzan},
editor = {AIRCC},
interhash = {f326f15f420c915153ee75196056f97a},
intrahash = {1e73a14a08a43a7b6d7aec6056363512},
issn = {ISSN : 2394 - 7527},
journal = {The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) },
keywords = {Huffman Vector character coding, evidence k-nearest neighbor, quantization,compression, theory.},
month = {April},
number = 1,
pages = 7,
timestamp = {2017-11-22T07:22:13.000+0100},
title = {COLOR SATELLITE IMAGE COMPRESSION USING THE EVIDENCE THEORY AND HUFFMAN CODING
},
url = {http://airccse.com/ijcsitce/papers/1114ijcsitce05.pdf},
volume = 1,
year = { 2014 }
}