Abstract: Now a day, everyone is fond of selfies. Not only selfies, man wishes to capture all his memorable events. This results in the increase of number of images and videos. It is obvious that a more amount of memory is needed to store all these images and videos.  If these images are needed to be transmitted, it even requires large bandwidth. So, there comes the need of image compression techniques. Image compression is a type of data compression applied to digital images, to reduce their cost for storage or transmission. These image compression techniques reduce the storage space occupied by the image without any loss to image quality. Thus, the image size can be reduced by selecting proper compression technique depending on the requirement of user or application. Algorithms may take advantage of visual perception and the statistical properties of image data to provide superior results compared with generic compression methods. The image size can be reduced by selecting proper compression technique depending on the requirement of user or application. Image compression is a technique in which the storage space of image is reduced   without degrading the image quality. We used SVD for the compression. In this, the image is compressed such that there is loss in image data, that is, image cannot be reconstructed if once compressed. This technique is best suited for normal photographs where a small loss of fidelity is acceptable. Most of the regular image compression techniques used today is lossy techniques. SVD is also a lossy image compression technique. SVD is robust and reliable orthogonal matrix decomposition method. Due to SVD conceptual and stability reasons, it becomes more and more popular in signal processing area. SVD has prominent properties in imaging. Although some SVD properties are fully utilized in image processing, others still need more investigation and contributed to. A key property of SVD is its relation to the rank of a matrix and its ability to approximate matrices of a given rank. Digital images are often represented by low rank matrices and, therefore, able to be described by a sum of a relatively small set of eigen images. This concept rises the manipulating of the signal as two distinct subspaces. SVD is an attractive algebraic transform for image processing. The method cum procedure using SVD and compress an image

 
Keywords: Image Enhancement, SVD, Brightness Level

| DOI: 10.17148/IJIREEICE.2019.8303