International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 39 - Issue 15 |
Published: February 2012 |
Authors: Mahmud Hasan, Kamruddin Md. Nur |
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Mahmud Hasan, Kamruddin Md. Nur . A Novel Spatial Domain Lossless Image Compression Scheme. International Journal of Computer Applications. 39, 15 (February 2012), 25-28. DOI=10.5120/4897-7429
@article{ 10.5120/4897-7429, author = { Mahmud Hasan,Kamruddin Md. Nur }, title = { A Novel Spatial Domain Lossless Image Compression Scheme }, journal = { International Journal of Computer Applications }, year = { 2012 }, volume = { 39 }, number = { 15 }, pages = { 25-28 }, doi = { 10.5120/4897-7429 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2012 %A Mahmud Hasan %A Kamruddin Md. Nur %T A Novel Spatial Domain Lossless Image Compression Scheme%T %J International Journal of Computer Applications %V 39 %N 15 %P 25-28 %R 10.5120/4897-7429 %I Foundation of Computer Science (FCS), NY, USA
In present Multimedia Computing era, compression of multimedia data has been highly significant. Digital Image, being a major part of multimedia data, requires effective compression mechanisms as well. During last few decades, there had been innovated numerous image compression techniques- most of which achieve considerable compression ratio by taking the advantage of domain transformation. Very few image compression algorithms are yet developed that can achieve appreciable compression ratio without transforming the image from the spatial domain. In this paper, we present a novel lossless image compression technique that does not require domain transformation. In spatial domain, it separates the image into blocks, performs some preprocessing and finds the Largest Differenced Pixel (LDP) value of a block. The amount of compression that can be achieved depends merely on the maximum differenced pixel value of the block. Apart from saving a notable amount of time required for domain transformation, experimental results over 200 standard images reveal that our proposed technique achieves 5.96% compression ratio on an average for gray-scale images while 11.69% average compression ratio for color images. Comparative studies of some particular test images prove the efficiency of the devised mechanism as compared to the existing spatial domain image compression algorithms. Finally, MSE calculation ensures the decompressed image is an exact approximation of the raw image considered for compression.