Image Compression Using Singular Value
Decomposition

Gowri.P; Senbaga Priya.K; Hari Prasath.R.K; Pavithra.S

doi:https://doi.org/10.14445/22315373/IJMTT-V65I8P507

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 65 | Issue 8 | Year 2019 | Article Id. IJMTT-V65I8P507 | DOI : https://doi.org/10.14445/22315373/IJMTT-V65I8P507

Image Compression Using Singular Value Decomposition

Gowri.P, Senbaga Priya.K, Hari Prasath.R.K, Pavithra.S

Citation :

Gowri.P, Senbaga Priya.K, Hari Prasath.R.K, Pavithra.S, "Image Compression Using Singular Value Decomposition," International Journal of Mathematics Trends and Technology (IJMTT), vol. 65, no. 8, pp. 74-81, 2019. Crossref, https://doi.org/10.14445/22315373/IJMTT-V65I8P507

Abstract

In this modern era, everyone is fond of taking photos and selfies. But, they look for their storage space running out and the speed of their device getting reduced. In that case, we have many algorithm based on image compression techniques. Here we are to discuss how Linear algebra plays its role in the compression of images. Basically, we will be discussing how SVD is extensively used in image compression process resulting in saving computer’s memory. The main technique used in this paper is SVD algorithm and is implemented using MATLAB. Using Singular value decomposition, JPEG file is compressed and the compression ratio is obtained. We apply linear algebra (SVD) on this matrix and get a reduced matrix out of the original matrix and the image that corresponds to the reduced matrix requires much lesser storage space compared to the original image.

Keywords

Image compression, singular value decomposition, techniques involved, JPEG imagecompression ratio, image coding

References

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