Journal :   International Journal of Technology

Volume No. :   4

Issue No. :  1

Year :  2014

Pages :   186-196

ISSN Print :  2231-3907

ISSN Online :  2231-3915


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A Novel Low Space Image Storing and Reconstruction Method by K-Means Clustering Algorithm



Address:   Santanu Halder1, Abul Hasnat2, Debotosh Bhattacharjee3, Mita Nasipuri3
1Department of Computer Science and Engineering, Government College of Engineering and Leather Technology, Kolkata, West Bengal
2Department of Computer Science and Engineering, Government College of Engineering and Textile Technology, Berhampore, West Bengal
3Department of Computer Science and Engineering, Jadavpur University, Kolkata-700032, West Bengal
*Corresponding Author
DOI No:

ABSTRACT:
This paper presents a lossy image compression technique that proposes a novel approach for storing RGB color images which save 33% memory space compared to memory space requirement of conventional method of storing RGB images. The proposed method, first finds the most and least dominating color components among three Red, Green and Blue color channels for each RGB image and then for each pixel of the image, it finds the absolute difference between the most and least dominating color values and expresses the difference as a fraction of the most dominating color value of the pixel. All the fraction values are clustered into sixteen groups using K-Means Clustering algorithm and all centroids are stored as Header. The less significant two bits of each the other two color channels are modified according to the cluster information. These two modified color channels along with one Header are stored for each image. Thus 33% of the memory space requirement to store the original image could be saved using the proposed method. At the time of reconstruction of the image, according to the cluster information third color component is retrieved with the help of the header. The experimental result shows that the reconstructed images retain around 98.5±0.5% of the original image information. The method has been implemented using Matlab 7 and tested on one standard FRAV2D database and hundred natural images and this method could be applied to compress any RGB image.
KEYWORDS:
Psycho-visual redundancy, Lossy image compression, Clustering, K-means, dominating color.
Cite:
Santanu Halder, Abul Hasnat, Debotosh Bhattacharjee, Mita Nasipuri. A Novel Low Space Image Storing and Reconstruction Method by K-Means Clustering Algorithm. Int. J. Tech. 4(1): Jan.-June. 2014; Page 186-196
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