Header menu link for other important links
X
Image compression using singular value decomposition
Swathi H.R, Sohini S, Surbhi,
Published in IOP Publishing
2017
Volume: 263
   
Issue: 4
Abstract
We often need to transmit and store the images in many applications. Smaller the image, less is the cost associated with transmission and storage. So we often need to apply data compression techniques to reduce the storage space consumed by the image. One approach is to apply Singular Value Decomposition (SVD) on the image matrix. In this method, digital image is given to SVD. SVD refactors the given digital image into three matrices. Singular values are used to refactor the image and at the end of this process, image is represented with smaller set of values, hence reducing the storage space required by the image. Goal here is to achieve the image compression while preserving the important features which describe the original image. SVD can be adapted to any arbitrary, square, reversible and non-reversible matrix of m × n size. Compression ratio and Mean Square Error is used as performance metrics. © Published under licence by IOP Publishing Ltd.
About the journal
JournalData powered by TypesetIOP Conference Series: Materials Science and Engineering
PublisherData powered by TypesetIOP Publishing
ISSN1757-8981
Open AccessYes