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Proceedings Paper

Application of DCT and binary matrix technique for color medical image compression
Author(s): G. Uma Vetri Selvi; R. Nadarajan
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Paper Abstract

This paper presents an application of discrete cosine transform compression technique on color medical images. The binary matrix technique is used to encode the coefficients. Initially the image is divided into R,G,B planes, discrete cosine transform(DCT), threshold function, quantization and binary matrix technique is applied to the planes. In the binary matrix technique the highest probable gray level is coded as zero in binary matrix and other gray levels with one. The most probable gray level is stored as first element in the array followed by other gray levels in the order as they appear in the image. The image is represented as binary matrix and gray level array. During decompression phase the binary matrix acts as look up table providing values for the reconstructed matrix. Since most of the values in individual planes are same after quantization this procedure yields good compression ratio. The tests of this lossy compression/decompression technique are performed on medical images, the obtained results Figure [3-11] shows that the DCT technique permits to considerably improve the compression rate while maintaining a good image quality when threshold varies in the interval: 0 ≤ TH ≤ 20 Q=8 for block sizes:[4×4]and [8×8].The computational complexity is greatly reduced thus producing faster compressions and decompressions. Due to the combination of simplicity, compression potential and image quality the proposed algorithm is best suitable for medical images.

Paper Details

Date Published: 26 February 2010
PDF: 6 pages
Proc. SPIE 7546, Second International Conference on Digital Image Processing, 75460V (26 February 2010); doi: 10.1117/12.853142
Show Author Affiliations
G. Uma Vetri Selvi, PSG College of Technology (India)
R. Nadarajan, PSG College of Technology (India)

Published in SPIE Proceedings Vol. 7546:
Second International Conference on Digital Image Processing
Kamaruzaman Jusoff; Yi Xie, Editor(s)

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