Image compression using singular value decomposition

[摘要] 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.

[发布机构] School of Computer Science and Engineering, VIT University, Vellore; 632014, India^1

[效力级别] 工业技术

[关键词] Data compression techniques;Digital image;Important features;Original images;Performance metrics;Reversible matrices;Singular values;Storage spaces