Digital Image Compression using DCT and SVD

Abstract

In our digital world, almost everybody cannot live without electronic devices i.e. mobile phones, computers, cameras, etc. As it is used in most of our daily lives i.e. jobs, rail stations, grocery stores, airports, etc. It plays a crucial role in our daily needs, and almost our images are stored as digital images in our device memory. So we need to have a large storage space for our data. So, image compression plays an important role in making our data size smaller by removing unnecessary objects (pixels or noises) in the images so as to maintain a larger amount of storage. Image compression is the method of data compression on digital images. The larger the data files associated with the images, thus the drives need for high compression ratios to make storage. For example, if we want to store larger files or videos (particularly movies) to our device memory, as each frame of a video is made of an image; thus the drives need to have high compression ratios in order to stores/transmits the data in an efficient form. Image compression process uses two techniques to compress an image- Lossy image compression- to achieve much higher compression, and Lossless image compression- to obtain zero quality loss images after compression. Lossy image compression techniques have much higher compression ratios than Lossless image compression. This paper describes Lossy compression techniques using Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) and deals with the comparative study of compressed images on the basis of different values.